Advanced Knowledge Technologies
Interdisciplinary Research Collaboration
Mid-term Review September 2003
Scientific report
Nigel
Shadbolt[1],
Fabio Ciravegna, John Domingue, Wendy Hall, Enrico Motta, Kieron O’Hara, David
Robertson, Derek Sleeman, Austin Tate, Yorick Wilks
1. Introduction
In a celebrated essay on the new electronic media, Marshall McLuhan wrote in 1962
Our private senses are not closed systems but are endlessly translated into each other in that experience which we call consciousness. Our extended senses, tools, technologies, through the ages, have been closed systems incapable of interplay or collective awareness. Now, in the electric age, the very instantaneous nature of co-existence among our technological instruments has created a crisis quite new in human history. Our extended faculties and senses now constitute a single field of experience which demands that they become collectively conscious. Our technologies, like our private senses, now demand an interplay and ratio that makes rational co-existence possible. As long as our technologies were as slow as the wheel or the alphabet or money, the fact that they were separate, closed systems was socially and psychically supportable. This is not true now when sight and sound and movement are simultaneous and global in extent. (McLuhan 1962, p.5, emphasis in original)[2]
Over forty years later, the seamless interplay that McLuhan demanded between our technologies is still barely visible. McLuhan’s predictions of the spread, and increased importance, of electronic media have of course been borne out, and the worlds of business, science and knowledge storage and transfer have been revolutionised. Yet the integration of electronic systems as open systems remains in its infancy.
The Advanced Knowledge Technologies IRC (AKT) aims to address this problem, to create a view of knowledge and its management across its lifecycle, to research and create the services and technologies that such unification will require. Half way through its six-year span, the results are beginning to come through, and this paper will explore some of the services, technologies and methodologies that have been developed. We hope to give a sense in this paper of the potential for the next three years, to discuss the insights and lessons learnt in the first phase of the project, to articulate the challenges and issues that remain.
2. The semantic web and knowledge management
The WWW provided the original context that made the AKT approach to knowledge management (KM) possible. AKT was initially proposed in 1999, it brought together an interdisciplinary consortium with the technological breadth and complementarity to create the conditions for a unified approach to knowledge across its lifecycle (Table 1). The combination of this expertise, and the time and space afforded the consortium by the IRC structure, suggested the opportunity for a concerted effort to develop an approach to advanced knowledge technologies, based on the WWW as a basic infrastructure.
|
AKT consortium member |
Expertise |
|
Aberdeen |
KBSs, databases, V&V |
|
Edinburgh |
Knowledge representation, planning, workflow modelling, ontologies |
|
OU |
Knowledge modelling, visualisation, reasoning services |
|
Sheffield |
Human language technology |
|
Southampton |
Multimedia, dynamic linking, knowledge acquisition, modelling, ontologies |
Table 1: Some of the specialisms of the AKT consortium
The technological context of AKT altered for the better in the short period between the development of the proposal and the beginning of the project itself with the development of the semantic web (SW), which foresaw much more intelligent manipulation and querying of knowledge. The opportunities that the SW provided for e.g., more intelligent retrieval, put AKT in the centre of information technology innovation and knowledge management services; the AKT skill set would clearly be central for the exploitation of those opportunities.
The SW, as an extension of the WWW, provides an interesting set of constraints to the knowledge management services AKT tries to provide. As a medium for the semantically-informed coordination of information, it has suggested a number of ways in which the objectives of AKT can be achieved, most obviously through the provision of knowledge management services delivered over the web as opposed to the creation and provision of technologies to manage knowledge.
AKT is working on the assumption that many web services will be developed and provided for users. The KM problem in the near future will be one of deciding which services are needed and of coordinating them. Many of these services will be largely or entirely legacies of the WWW, and so the capabilities of the services will vary. As well as providing useful KM services in their own right, AKT will be aiming to exploit this opportunity, by reasoning over services, brokering between them, and providing essential meta-services for SW knowledge service management.
Ontologies will be a crucial tool for the SW. The AKT consortium brings a lot of expertise on ontologies together, and ontologies were always going to be a key part of the strategy. All kinds of knowledge sharing and transfer activities will be mediated by ontologies, and ontology management will be an important enabling task. Different applications will need to cope with inconsistent ontologies, or with the problems that will follow the automatic creation of ontologies (e.g. merging of pre-existing ontologies to create a third). Ontology mapping, and the elimination of conflicts of reference, will be important tasks. All of these issues are discussed along with our proposed technologies.
Similarly, specifications of tasks will be used for the deployment of knowledge services over the SW, but in general it cannot be expected that in the medium term there will be standards for task (or service) specifications. The brokering meta-services that are envisaged will have to deal with this heterogeneity.
The emerging picture of the SW is one of great opportunity but it will not be a well-ordered, certain or consistent environment. It will comprise many repositories of legacy data, outdated and inconsistent stores, and requirements for common understandings across divergent formalisms. There is clearly a role for standards to play to bring much of this context together; AKT is playing a significant role in these efforts (section 5.1.6 of Management Report). But standards take time to emerge, they take political power to enforce, and they have been known to stifle innovation (in the short term). AKT is keen to understand the balance between principled inference and statistical processing of web content. Logical inference on the Web is tough. Complex queries using traditional AI inference methods bring most distributed computer systems to their knees. Do we set up semantically well-behaved areas of the Web? Is any part of the Web in which semantic hygiene prevails interesting enough to reason in? These and many other questions need to be addressed if we are to provide effective knowledge technologies for our content on the web.
3. AKT knowledge lifecycle: the challenges
Since AKT is concerned with providing the tools and services for managing knowledge throughout its lifecycle, it is essential that it has a model of that lifecycle. The aim of the AKT knowledge lifecycle is not to provide, as most lifecycle models are intended to do, a template for knowledge management task planning. Rather, the original conceptualisation of the AKT knowledge lifecycle was to understand what the difficulties and challenges there are for managing knowledge whether in corporations or within or across repositories.
The AKT conceptualisation of the knowledge lifecycle comprises six challenges, those of acquiring, modelling, reusing, retrieving, publishing and maintaining knowledge (O’Hara 2002, pp.38-43). The six challenge approach does not come with formal definitions and standards of correct application; rather the aim is to classify the functions of AKT services and technologies in a straightforward manner.
Figure 1: AKT's six knowledge challenges
This paper will examine AKT’s current thinking on these challenges. An orthogonal challenge, when KM is conceived in this way (indeed, whenever KM is conceived as a series of stages) is to integrate the approach within some infrastructure. Therefore the discussion in this paper will consider the challenges in turn (sections 4-9), followed by integration and infrastructure (section 10). We will then see the AKT approach in action, as applications are examined (section 11). Theoretical considerations (section 12) and future work (section 13) conclude the review.
4. Acquisition
Traditionally, in knowledge engineering, knowledge acquisition (KA) has been regarded as a bottleneck (Shadbolt & Burton, 1990). The SW has exacerbated this bottleneck problem; it will depend for its efficacy on the creation of a vast amount of annotation and metadata for documents and content, much of which will have to be created automatically or semi-automatically, and much of which will have to be created for legacy documents by people who are not those documents’ authors.
KA is not only the science of extracting information from the environment, but rather of finding a mapping from the environment to concepts described in the appropriate modelling formalism. Hence, the importance of this for acquisition is that – in a way that was not true during the development of the field of KA in the 1970s and 80s – KA is now focused strongly around the acquisition of ontologies. This trend is discernable in the evolution of methodologies for knowledge intensive modelling (Schreiber et al, 2000).
Therefore, in the context of the SW, an important aspect of KA is the acquisition of knowledge to build and populate ontologies, and furthermore to maintain and adapt ontologies to allow their reuse, or to extend their useful lives. Particular problems include the development and maintenance of large ontologies, creating and maintaining ontologies by exploiting the most common, but relatively intractable, source of natural language texts. However, the development of ontologies is also something that can inform KA, by providing templates for acquisition.
AKT has a number of approaches to the KA bottleneck, and in a paper of this size it is necessary to be selective (this will be the case for all the challenges). In this section, we will chiefly discuss the harvesting and capture of large scale content from web pages and other resources, (section 4.1), content extraction of ontologies from text (section 4.2), and the extraction of knowledge from text (section 4.3). These approaches constitute the AKT response to the new challenges posed by the SW; however, AKT has not neglected other, older KA issues. A more traditional, expert-oriented KA tool approach, will be discussed in section 4.4.
4.1. Harvesting
AKT includes in its objectives the investigation of technologies to process a variety of knowledge on a web scale. There are currently insufficient resources marked up with meta-content in machine-readable form. In the short to medium term we cannot see such resources becoming available. One of the important objectives is to have up to date information, and so the ability to regularly harvest, capture and update content is fundamental. There has been a range of activities to support large-scale harvesting of content.
4.1.1 Early harvesting
Scripts were written to “screen scrape” university web sites (the leading CS research departments were chosen), using a new tool Dome (Leonard & Glaser 2001), that is an output of the research of an EPSRC student.
Dome is a programmable XML/HTML editor. Users load in a page from the
target site and record a sequence of editing operations to extract the desired
information. This sequence can then be replayed automatically on the rest of
the site's pages. If irregularities in the pages are discovered during this
process, the program can be paused and amended to cope with the new input.
We see below (Figure 2) the system running, and processing a personal
web page, also shown. A Dome program has been recorded which removes all
unnecessary elements from the source of this page, leaving just the desired
data, and the element names and layout have been changed to the desired output
format, RDF.
Figure 2: A Dome Script to produce RDF from a Web Page
Other scripts have been written using appropriate standard programming tools to harvest data from other sources. These scripts are run on a nightly basis to ensure that the information we glean is as up to date as possible. As the harvesting has progressed, it has also been done by direct access to databases, where possible. In addition, other sites are beginning to provide RDF to us directly, as planned.
The theory behind this process is that of a bootstrap. Initially, AKT harvests from the web without involving the personnel at the sources at all. (This also finesses any problems of Data Protection, since all information is publicly available.) Once the benefits to the sources of having their information harvested becomes clear, some will contact us to cooperate. The cooperation can take various forms, such as sending us the data or RDF, or making the website more accessible, but the preferred solution is for them to publish the data on their website on a nightly basis in RDF (according to our ontology). These techniques are best suited to data which is well-structured (such as university and agency websites), and especially that which is generated from an underlying database.
As part of the harvesting activity, and as a service to the community, the data was put in almost raw form on a website registered for the purpose: www.hyphen.info. Figure 3 shows a snapshot of the range of data we were able to make available in this form.
Figure 3: www.hyphen.info CS UK Page
4.1.2 Late harvesting
The techniques above will continue to be used for suitable data sources. A knowledge mining system to extract information from several sources automatically has also been built (Armadillo – cf section 7.2.2), exploiting the redundancy found on the Internet, apparent in the presence of multiple citations of the same facts in superficially different formats. This redundancy can be exploited to bootstrap the annotation process needed for IE, thus enabling production of machine-readable content for the SW. For example, the fact that a system knows the name of an author can be used to identify a number of other author names using resources present on the Internet, instead of using rule-based or statistical applications, or hand-built gazetteers. By combining a multiplicity of information sources, internal and external to the system, texts can be annotated with a high degree of accuracy with minimal or no manual intervention. Armadillo utilizes multiple strategies (Named Entity Recognition, external databases, existing gazetteers, various information extraction engines such as Amilcare – section 7.1.1 – and Annie) to model a domain by connecting different entities and objects.
4.2. Extracting ontologies from text: Adaptiva
Existing ontology construction methodologies involve high
levels of expertise in the domain and the encoding process. While a great deal of effort is going into the
planning of how to use ontologies, much less has been achieved with respect to
automating their construction. We need a feasible computational process to
effect knowledge capture.
The
tradition in ontology construction is that it is an entirely manual process.
There are large teams of editors or, so-called, ‘knowledge managers’ who are
occupied in editing knowledge bases for eventual use by a wider community in
their organisation. The process of knowledge capture or ontology construction
involves three major steps: first, the construction of a concept hierarchy;
secondly, the labeling of relations between concepts, and thirdly, the
association of content with each node in the ontology (Brewster et al 2001a).
In the
past a number of researchers have proposed methods for creating conceptual
hierarchies or taxonomies of terms by processing texts. The work has sought to
apply methods from Information Retrieval (term distribution in documents) and
Information Theory (mutual information) (Brewster 2002). It is relatively easy
to show that two terms are associated in some manner or to some degree of
strength. It is possible also to group terms into hierarchical structures of
varying degree of coherence. However, the most significant challenge is to be
able to label the nature of the relationship between the terms.
This has led to the development of Adaptiva (Brewster et al 2001b), an ontology building environment which implements a user-centred approach to the process of ontology learning. It is based on using multiple strategies to construct an ontology, reducing human effort by using adaptive information extraction. Adaptiva is a Technology Integration Experiment (TIE – section 3.1 of the Management Report).
The ontology learning process starts with the provision of a seed ontology, which is either imported to the system, or provided manually by the user. A seed may consist of just two concepts and one relationship. The terms used to denote concepts in the ontology are used to retrieve the first set of examples in the corpus. The sentences are then presented to the user to decide whether they are positive or negative examples of the ontological relation under consideration.
In Adaptiva, we have integrated Amilcare (discussed in greater detailed below in section 7.1.1). Amilcare is a tool for adaptive Information Extraction (IE) from text designed for supporting active annotation of documents for Knowledge Management (KM). It performs IE by enriching texts with XML annotations. The outcome of the validation process is used by Amilcare, functioning as a pattern learner. Once the learning process is completed, the induced patterns are applied to an unseen corpus and new examples are returned for further validation by the user. This iterative process may continue until the user is satisfied that a high proportion of exemplars is correctly classified automatically by the system.
Using Amilcare, positive and negative examples are
transformed into a training corpus where XML annotations are used to identify
the occurrence of relations in positive examples. The learner is then launched
and patterns are induced and generalised. After testing, the best, most
generic, patterns are retained and are then applied to the unseen corpus to
retrieve other examples. From Amilcare’s point of view the task of ontology
learning is transformed into a task of text annotation: the examples are
transformed into annotations and annotations are used to learn how to reproduce
such annotations.
Experiments are under way to
evaluate the effectiveness of this approach. Various factors such as size and
composition of the corpus have been considered. Some experiments indicate that,
because domain specific corpora take the shared ontology as background
knowledge, it is only by going beyond the corpus that adequate explicit
information can be identified for the acquisition of the relevant knowledge
(Brewster et al. 2003). Using the principles underlying the Armadillo
technology (cf. Section 7.2.2), a model has been proposed for a web-service, which will
identify relevant knowledge sources outside the specific domain corpus thereby
compensating for the lack of explicit specification of the domain knowledge.
4.3. KA from text: Artequakt
Given the amount of content on the web there is every likelihood that in some domains the knowledge that we might want to acquire is out there. Annotations on the SW could facilitate acquiring such knowledge, but annotations are rare and in the near future will probably not be rich or detailed enough to support the capture of extended amounts of integrated content. In the Artequakt work we have developed tools able to search and extract specific knowledge from the Web, guided by an ontology that details what type of knowledge to harvest. Artequakt is an Integrated Feasibility Demonstrator (IFD) that combines expertise and resources from three projects – Artiste, the Equator and AKT IRCs.
Many information extraction (IE) systems rely on predefined templates and pattern-based extraction rules or machine learning techniques in order to identify and extract entities within text documents. Ontologies can provide domain knowledge in the form of concepts and relationships. Linking ontologies to IE systems could provide richer knowledge guidance about what information to extract, the types of relationships to look for, and how to present the extracted information. We discuss IE in more detail in section 7.1.
There exist many IE systems that enable the recognition of entities within documents (e.g. ‘Renoir’ is a ‘Person’, ‘25 Feb 1841’ is a ‘Date’). However, such information is sometimes insufficient without acquiring the relation between these entities (e.g. ‘Renoir’ was born on ‘25 Feb 1841’). Extracting such relations automatically is difficult, but crucial to complete the acquisition of knowledge fragments and ontology population.
When analysing documents and extracting information, it is inevitable that duplicated and contradictory information will be extracted. Handling such information is challenging for automatic extraction and ontology population approaches.
Artequakt (Alani et al 2003b, Kim et al 2002) implements a system that searches the Web and extracts knowledge about artists, based on an ontology describing that domain. This knowledge is stored in a knowledge base to be used for automatically producing tailored biographies of artists.
Artequakt's architecture (Figure 4) comprises of three key areas. The first concerns the knowledge extraction tools used to extract factual information items from documents and pass them to the ontology server. The second key area is the information management and storage. The information is stored by the ontology server and consolidated into a knowledge base that can be queried via an inference engine. The final area is the narrative generation. The Artequakt server takes requests from a reader via a simple Web interface. The reader request will include an artist and the style of biography to be generated (chronology, summary, fact sheet, etc.). The server uses story templates to render a narrative from the information stored in the knowledge base using a combination of original text fragments and natural language generation.
Figure 4: Artequakt's architecture
The first stage of this project consisted of developing an ontology for the domain of artists and paintings. The main part of this ontology was constructed from selected sections in the CIDOC Conceptual Reference Model ontology. The ontology informs the extraction tool of the type of knowledge to search for and extract. An information extraction tool was developed and applied that automatically populates the ontology with information extracts from online documents. The information extraction tool makes use of an ontology, coupled with a general-purpose lexical database, WordNet and an entity-recogniser, GATE (Cunningham et al 2002 – see section 10.4) as guidance tools for identifying knowledge fragments consisting not just of entities, but also the relationships between them. Automatic term expansion is used to increase the scope of text analysis to cover syntactic patterns that imprecisely match our definitions.
Figure
5: The IE process in Artequakt
The extracted information is stored in a knowledge base and analysed for duplications and inconsistencies. A variety of heuristics and knowledge comparison and term expansion methods were used for this purpose. This included the use of simple geographical relations from WordNet to consolidate any place information; e.g. places of birth or death. Temporal information was also consolidated with respect to precision and consistency.
Narrative construction tools were developed that queried the knowledge base through an ontology server. These queries searched and retrieved relevant facts or textual paragraphs and generated a specific biography. The challenge is to build biographies for artists where there is sparse information available, distributed across the Web. This may mean constructing text from basic factual information gleaned, or combining text from a number of sources with differing interests in the artist. Secondly, the work also aspires to provide biographies that are tailored to the particular interests and requirements of a given reader. These might range from rough stereotyping such as “A biography suitable for a child” to specific reader interests such as “I'm interested in the artists’ use of colour in their oil paintings” (Figure 6).
Figure 6: The biography generation process in Artequakt
Figure
7: Artequakt-generated
biography for Renoir
The system is undergoing evaluation and testing at the moment. It has already provided important components for a successful bid (the SCULPTEUR project) into the EU VI Framework.
4.4. Refiner++
Refiner++ (Aiken & Sleeman 2003) is a new implementation of Refiner+ (Winter & Sleeman 1995), an algorithm that detects inconsistencies in a set of examples (cases) and suggests ways in which these inconsistencies might be removed. The domain expert is required to specify which category each case belongs to; Refiner+ then infers a description for each of the categories and reports any inconsistencies that exist in the dataset. An inconsistency is when a case matches a category other than the one in which the expert has classified it. If inconsistencies have been detected in the dataset, the algorithm attempts to suggest appropriate ways of dealing with the inconsistencies by refining the dataset. At the time of writing, the Refiner++ system has been presented to three experts to use on problems in their domains: anaesthetics, educational psychology, and intensive care.
Although the application can be used to import existing datasets and perform analysis on them, its real strength is for an expert who wants to conceptualize a domain where the inherent task is classification. Refiner++ requires the expert to articulate cases, specifying the descriptors they believe to be important in their domain. This causes the expert to conceptualize their domain, bringing out the hidden relationships between descriptors that might otherwise be ignored.
We hope to produce a “refinement workbench” to include Refiner++, ReTax (Alberdi & Sleeman 1997) and ConRef (Winter et al 1998 – and section 9.2).
5. Modelling
As noted in the previous section, ontologies loom large in AKT – as in the SW – for modelling purposes. In particular, we have already seen the importance of ontologies (a) for directing acquisition, and (b) as objects to be acquired or created. The SW, as we have argued, will be a domain in which services will be key. For particular tasks, agents are likely to require combinations of services, either in parallel or sequentially. In either event, ontologies are going to be essential to provide shared understandings of the domain, preconditions and postconditions for their application and optimal combination. However, in the likely absence of much standardisation, ontologies are not going to be completely shared.
Furthermore, it will not be possible to assume unique or optimal solutions to the problem of describing real-world contexts. Ontologies will be aimed at different tasks, or will make inconsistent, but reasonable, assumptions. Given two ontologies precisely describing a real-world domain, it will not in general be possible to guarantee mappings between them without creating new concepts to augment them. As argued in (Kalfoglou & Schorlemmer 2003a), and section 9.3 above, there is a distinct lack of formal underpinnings here. Ontology mapping will be an important aspect to knowledge modelling, and as we have already seen, AKT is examining these issues closely.
Similarly, the production of ontologies will need to be automated, and documents will become a vital source for ontological information. Hence tools such as Adaptiva (section 4.2) will, as we have argued, be essential. However, experimental evidence amassed during AKT shows that in texts, it is often the essential ontological information that is not expressed, since it is taken to be part of the ‘background knowledge’ implicitly shared by reader and author (Brewster et al 2003). Hence the problem of how to augment information sources for documents is being addressed by AKT (Brewster et al 2003).
A third issue is that of the detection of errors in automatically-extracted ontologies, particularly from unstructured material. It was for these reasons that we have also made some attempts to extract information from semi-structured sources ie programs and Knowledge Bases (Sleeman et al, 2003).
In all these ways, there are plenty of unresolved research issues with respect to ontologies that AKT will address over the remaining half of its remit. However, modelling is a fundamental requirement in other areas, for instance with respect to the modelling of business processes in order to achieve an understanding of the events that a business must respond to. The AKT consortium has amassed a great deal of experience of modelling processes such as these that describe the context in which organisations operate. Section 5.1 looks at the use of protocols to model service interactions, while in section 5.2 we will briefly discuss one use of formal methods to describe lifecycles.
5.1. Service interaction protocol
To address this problem, the notion of service interaction protocols has been developed. These are formal structures representing distributed dialogues which serve to impose social conventions or norms on agent interactions (cf the communications policy work of Bradshaw and his colleagues at IHMC). A protocol specifies all possible sequences of messages that would allow the dialogue to be conducted successfully within the boundaries established by the norm. All agents engaging in the interaction are responsible for maintaining this dialogue, and the updated dialogue is passed in its entirety with each communication between agents. Placing messages in the context of the particular norm to which they relate in this manner allows the agents to understand the current state of the interaction and locate their next roles within it, and so makes the interactions in the environment more resistant to the problems of asynchrony.
Furthermore, since
these protocols are specified in a formal manner, they can be subjected to
formal model checking as well as empirical (possibly synchronous) ‘off-line’
testing before deployment. In addition to proving certain properties of dialogues
are as desired, this encourages the exploration of alternative descriptions of
norms and the implications these would have for agent interactions (Vasconcelos
et.al. 2002, Walton & Robertson 2002).
In addition to this work on service interaction protocols, we have also encountered the issues of service choreography in an investigation of the interactions between the Semantic Web, the agent-based computing paradigm and the Web Services environment.
The predominant communications abstraction in the agent environment is that of speech acts or performatives, in which inter-agent messages are characterised according to their perlocutionary force (the effect upon the listener). Although the Web Services environment does not place the same restrictions on service providers as are present on agents (in which an agent's state is modelled in terms of its beliefs, desires and intentions, for example), the notion of performative-based messages allows us abstract the effects, and expectations of effect, of communications.
The set of speech acts which comprise an agent's communicative capabilities in an agent-based systems is known as an agent communication language. In (Gibbins et al 2003), we describe the adaptation of the DAML Services ontology for Web Service description to include an agent communication language component. This benefits service description and discovery by separating the application domain-specific contents of messages from their domain-neutral pragmatics, and so simplifying the design of brokerage components which match service providers to service consumers. This work was carried out in collaboration with QinetiQ, and was realised in a prototype system for situational awareness in a simulated humanitarian aid scenario.
Formal models provide an interesting method for understanding business processes. In the next section, we look at their use to describe knowledge system lifecycles.
5.2. A lifecycle calculus
Knowledge-intensive systems have lifecycles. They are created through processes of knowledge acquisition and problem solver design and reuse; they are maintained and adapted; and eventually they are decommissioned. In software engineering, as well as in the more traditional engineering disciplines, the study of such processes and their controlled integration through the lifetime of a product is considered essential and provides the basis for routine project management activities, such as cost estimation and quality management. As yet, however, we have not seen the same attention to life cycles in knowledge engineering.
Our current need to represent and reason formally about knowledge lifecycles is spurred by the Internet, which is changing our view of knowledge engineering. In the past we built and deployed reasoning systems which typically were self-contained, running on a single computer. Although these systems did have life cycles of design and maintenance, it was only necessary for these to be understood by the small team of engineers who actually were supporting each system. This sort of understanding is close to traditional software engineering so there was no need to look beyond traditional change management tools to support design. Formal representation and reasoning was confined to the artefacts being constructed, not to the process of constructing them. This situation has changed. Ontologies, knowledge bases and problem solvers are being made available globally for use (and reuse) by anyone with the understanding to do so. But this raises the problem of how to gain that understanding. Even finding appropriate knowledge components is a challenge. Assembling them without any knowledge of their design history is demanding. Maintaining large assemblies of interacting components (where the interactions may change the components themselves) is impossible in the absence of any explicit representation of how they have interacted.
5.2.1 The value of formality
There is, therefore, a need for formality in order to be able to provide automated support during various stages of the knowledge-management life cycle. The aim of formality in this area is twofold: to give a concise account of what is going on, and to use this account for practical purposes in maintaining and analysing knowledge-management life cycles. If, as envisioned by the architects of the Semantic Web, knowledge components are to be made available on the Internet for humans and machines to use and reuse, then it is natural to study and record the sequences of transformations performed upon knowledge components. Agents with the ability to understand these sequences would be able to know the provenance of a body of knowledge and act accordingly, for instance, by deciding their actions depending on their degree of trust in the original source of a body of knowledge, or of the specific knowledge transformations performed on it.
Different sorts of knowledge transformations preserve different properties of the components to which they are applied. Being able to infer such property-preservation from the structure of a life cycle of a knowledge component may be useful for agents, as it can help them decide which reasoning services to use in order to perform deductions without requiring the inspection of the information contained in knowledge components themselves.
Knowing whether these kinds of properties are preserved across life cycles would be useful, especially in environments such as the WWW, where knowledge components are most likely to be translated between different languages, mapped into different ontologies, and further specialised or generalised in order to be reused together in association with other problem solvers or in other domains. Thus, having a formal framework with a precise semantics in which we could record knowledge transformations and their effect on certain key properties would allow for the analysis and automation of services that make use of the additional information contained in life-cycle histories.
5.2.2 The AKT approach
We have been exploring a formal approach to the understanding of lifecycles in knowledge engineering. Unlike many of the informal life-cycle models in software engineering, our approach allows for a high level of automation in the use of lifecycles. When supplied with appropriate task-specific components, it can be deployed to fully automate life-cycle processes. Alternatively, it can be used to support manual processes such as reconstruction of chains of system adaptation. We have developed a formal framework for describing life cycles and mechanisms for using these by means of a lifecycle calculus of abstract knowledge transformations with which to express life cycles of knowledge components.
To allow us to operate at an abstract level, without committing ourselves to a particular knowledge representation formalism or a particular logical system, we have based our treatment of knowledge transformation on abstract model theory. In particular, we use institutions and institution morphisms (Goguen & Burstall 1992) as mathematical tools upon which to base a semantics of knowledge components and their transformations. An institution captures the essential aspects of logical systems underlying any specification theory and technology. In practice, the idea of a single one-size-fits-all life-cycle model is implausible. Applications of formal knowledge life cycles may use more specialised calculi and use these to supply different degrees of automated support.
In (Schorlemmer et al 2002a) we show how to reason about properties that are or are not preserved during the life cycle of a knowledge component. Such information may be useful for the purposes of high-level knowledge management. If knowledge services that publicise their capabilities in distributed environments, such as the Web, also define and publicise the knowledge transformations they realise in terms of a formal language, then automatic brokering systems may use this additional information in order to choose among several services according to the properties one would like to preserve.
In (Schorlemmer et al 2002b) we analyse a real knowledge-engineering scenario consisting of the life cycle of an ontology for ecological meta-data, and describe it it terms of our life-cycle calculus. We show how this could be done easily with the support of a life-cycle editing tool, F-Life (Robertson & Schorlemmer 2003), that constructs formal life-cycle patterns by composing various life cycle rules into a set of Horn clauses that constitute a logic program. This program can then be used to recreate -with a meta-interpreter - analogous life-cycles, following the same steps we have previously compiled by means of the editor. We also describe an architecture in which the brokering of several knowledge services in a distributed environment is empowered by the additional information we obtain from formal life-cycle patterns. In particular we show how the previously edited abstract life-cycle pattern can be used to guide a brokering system in the task of choosing the appropriate problem solvers in order to execute a concrete sequence of life-cycle steps. The information of the concrete life cycle that is followed is then stored alongside the transformed knowledge component, so that this information may subsequently be used by other knowledge services.
6. Reuse
Reuse is, of course, a hallowed principle of software engineering, that has certainly been adopted in knowledge management. And, of course, given the problems of knowledge acquisition– the KA bottleneck – and the difficulties with, for example, the creation and maintenance of ontologies that we have already noted in earlier sections, it clearly makes sense to reuse expensively-acquired knowledge or models etc in KM.
However, as always, such things are easier said than done. Many KM artefacts are laboriously handcrafted, and as such require a lot of rejigging for new contexts. Automatically-generated material also carries its own problems. Selection of material to reuse is also a serious issue. But with all the knowledge lying around, say, on the WWW, the power of the resource is surely too great to be ignored. Hence reuse is a major knowledge challenge in its own right, which AKT has been investigating. As one example, AKT has been investigating the reuse of expensively-acquired constraints, and also of various pre-existing knowledge services, in the management of a virtual organization (section 6.1). Furthermore, if services and/or resources are to be reused, then the user will technically have a large number of possible services for any query – if queries are composed, the space of possible combinations could become very large. Hence, brokering services will be of great importance, and AKT’s investigations of this concept are reported in section 6.2. Work will also have to be done on the modification and combination of knowledge bases, and we will see tools for this in section 6.3.
6.1. KRAFT/I-X
Virtual organizations are the enabling enterprise structure in modern e-business, e-science, and e-governance. Such organizations most effectively harness the capabilities of individuals working in different places, with different expertise and responsibilities. Through the communication and computing infrastructure of the virtual organization, these people are able to work collaboratively to accomplish tasks, and together to achieve common organisational goals. In the KRAFT/I-X Technology Integration Experiment (TIE), a number of knowledge-based technologies are integrated to support workers in a virtual organization (cf Figure 8):
· Workflow and business process modelling techniques (Chen-Burger and Stader 2003, Chen-Burger et al 2002) provide the coordination framework to facilitate smooth, effective collaboration among users;
· Task-supporting user interfaces (I-X process panels – Tate 2003, and http://www.aktors.org/technologies/ix/).
· Constraint interchange and solving techniques (Gray et al 2001, Hui et al 2003) guide users towards possible solutions to shared problems, and keep the overall state of the work activity consistent;
· Agent-based infrastructure provides the underlying distributed, heterogeneous software architecture (the AKTbus – http://www.aktors.org/technologies/aktbus/).
Figure 8: The I-X Tools include: 1. Process Panel (I-P2); 2. Domain Editor (I-DE): create and modify process models; 3 I-Space: maintain relationships with other agents; 4 Messenger: instant messaging tool, for both structured and less formal communications; 5 Issue Editor: create, modify, annotate issues.
As a simple example of an application that the KRAFT/I-X TIE can support, consider a Personal Computer purchasing process in an organization. There will typically be several people involved, including the end-user who needs a PC, a technical support person who knows what specifications and configurations are possible and appropriate, and a financial officer who must ensure that the PC is within budget. In the implemented KRAFT/I-X demonstrator, the second and third of these people are explicitly represented: the technical support by a process panel running in Aberdeen (ABDN-panel, Figure 9) and the finance officer by a panel running in Edinburgh (ED-panel). Note that the user is represented implicitly by the PC requirements input to the system through the ED-panel. The two panels share a workflow/business process model that enables them to cooperate. As part of this workflow, the ED-panel passes user requirement constraints to the ABDN-panel, so that a feasible technical configuration for the PC can be identified. In fact, the ABDN-panel uses a knowledge base of PC configurations, and a constraint-solving system (KRAFT) to identify the feasible technical configuration, which is then passed back to the ED-panel via the ABDN-panel.
Figure 9: KRAFT-I/X demonstrator architecture
6.2. The AKT broker
In order to match service requests with appropriate Semantic Web services (and possibly sequences of those services), some sort of brokering mechanism would seem to be needed. Service-providing agents advertise to this broker a formal specification of each offered service in terms of its inputs, outputs, preconditions, effects, and so on. This specification is constructed using elements from one or more shared ontologies, and is stored within the broker. When posted to the broker, a request – in the form of the specification of the desired service – is compared to the available services for potential matches (and it may be possible – and sometimes necessary – to compose sequences of several services to meet certain requests).
However, this approach to service brokering raises a number of practical questions. As for all techniques dependent on shared ontologies, the source and use of these ontologies is an issue. And with brokering there is a particular problem concerning the appropriate content of service specifications: rich domain ontologies make possible rich specifications – and also increase the possible search space of services and the reasoning effort required to determine if service and request specification match. One solution to this, it might be thought, is to constrain the ontologies to describe very specific service areas, thereby constraining the specification language. Some focusing of ontologies in this manner may be desirable, resulting in a broker that is specialised for particular services or domains rather than being general-purpose. However, if the constraints placed on ontologies are too great this will result in very specialised brokers, and would have the effect of shifting the brokering problem from one of finding appropriate services to one of finding appropriate service brokers – and so, some sort of ‘meta-brokering’ mechanism would be necessary, and the brokering problem would have to be addressed all over again.
While careful ontological engineering would appear unavoidable, alternative approaches to this problem that we have been investigating involve using ideas emerging elsewhere in the project to prune the search space. For example, by encouraging the description of services in terms of the lifecycle calculus (section 5.2), where appropriate, to complement their specifications, allows additional constraints to be placed on service requests and the search for matching services to be focused upon those conforming to these constraints. Likewise, considering the brokering task as being, in effect, one of producing an appropriate service interaction protocol for the request, can serve to concentrate the search on to those services that are willing and able to engage in such protocols.
6.3. Reusing knowledge bases
6.3.1 MUSKRAT
MUSKRAT (Multistrategy Knowledge Refinement and Acquisition Toolbox – White & Sleeman 2000) aims to unify problem solving, knowledge acquisition and knowledge-base refinement in a single computational framework. Given a set of Knowledge Bases (KBs) and Problem Solvers (PSs), the MUSKRAT-Advisor investigates whether the available KBs will fulfil the requirements of the selected PS for a given problem. We would like to reject impossible combinations KBs and PSs quickly. We represent combinations of KBs and PSs as CSPs. If a CSP is not consistent, then the combination does not fulfil the requirements. The problem then becomes one of quickly identifying inconsistent CSPs. To do this, we propose to relax the CSPs: if we can prove that the relaxed version is inconsistent then we know that the original CSP is also inconsistent. It is not obvious that solving relaxed CSPs is any easier. In fact, phase transition research (e.g. Prosser 1994) seems to indicate the opposite when the original CSP is inconsistent. We have experimented with randomly generated CSPs (Nordlander et al 2002), where the tightness of the constraints in a problem varies uniformly. We have shown that careful selection of the constraints to relax can save up to 70% of the search time. We have also investigated practical heuristics for relaxing CSPs. Experiments show that the simple strategy of removing constraints of low tightness is effective, allowing us to save up to 30% of the time on inconsistent problems without introducing new solutions.
In the constraints area, future work will look at extending this approach to more realistic CSPs. The focus will be on scheduling problems, which are likely to involve non-binary and global constraints, and constraint graphs with particular properties (e.g. Walsh 2001). We will also investigate more theoretical CSP concepts, including higher consistency levels and problem hardness. Success in this research will allow us to apply constraint satisfaction and relaxation techniques to the problem of knowledge base reuse.
6.3.2 ConcepTool
ConcepTool is an Intelligent Knowledge Management Environment for building, modifying, and combining expressive domain knowledge bases and application ontologies. Apart from its user-oriented editing capabilities, one of the most notable features of the system is its extensive automated support to the analysis of knowledge being built, modified or combined. ConcepTool uses Description Logic-based taxonomic reasoning to provide analysis functionalities such as KB consistency, detection of contradicting concepts, making explicit of hidden knowledge and ontology articulation.
The development of the core ConcepTool system has been funded on a separate grant by the EPSRC, while the development of the articulation functionalities has been funded by the AKT IRC consortium. Notably, two systems have been actually developed: the first one, which supported modelling and analysis on an expressive Enhanced Entity-Relationship knowledge model, has been used as a prototype for the development of the second one, which uses a frame-based model. Both versions of ConcepTool can handle complex, sizeable ontologies (such as the AKT one), supporting the combination of heterogeneous knowledge sources by way of taxonomic, lexical and heuristic analysis.
7. Retrieval
Given the amount of information available on the WWW, clearly a major problem is retrieving that information from the noise which surrounds it. Retrieval from large repositories is a major preoccupation for AKT. There is a major trend, supported by the Semantic Web, towards annotating documents, which should enable more intelligent retrieval (section 7.2). Furthermore, such annotations will facilitate the difficult problem, already apparent under several of our headings above, of ontology population.
However, annotation itself will not solve all the problems of information retrieval. Information is often dispersed, or distributed, around large unstructured repositories – like the WWW itself – in such a way as to make systematic retrieval impossible, and intelligent retrieval difficult. Information may indeed only be implicit in repositories, in which case retrieval must include not only the ability to locate the important material, but also the ability to perform inference on it (while avoiding circularity – how does one identify the important information prior to inferring about the representation that contains it in implicit form?). As well as unstructured, distributed repositories, information can also be hidden in unstructured formats, such as plain text or images (section 7.1).
However, even information held in relatively structured formats can be hard to get at, often because it is implicit. One issue that AKT has been addressing here is that of extracting information from ontologies about structures within organisations, in particular trying to extract implicit information about informal communities of interest or practice based on more formal information about alliances, co-working practices, etc (section 7.3).
7.1. Ontology-based information extraction
7.1.1 Amilcare
Information
extraction from text (IE) is the process of populating a structured information
source (e.g. an ontology) from a semi-structured, unstructured, or free text,
information source. Historically, IE has been seen as the process of extracting
information from newspaper-like texts to fill a template, i.e. a form
describing the information to be extracted.
We have
worked in the direction of extending the coverage of IE first of all to
different types of textual documents, from rigidly structured web pages (e.g.
as generated by a database) to completely free (newspaper-like) texts, with
their intermediate types and mixtures (Ciravegna 2001a).
Secondly
we have worked on the use of machine learning for allowing porting to different
applications using domain specific (non linguistic) annotation. The result is
the definition of an algorithm—called (LP)2 (Ciravegna 2001b) and
(Ciravegna 2001c) — able to cope with a number of types of IE tasks on
different types of documents using only domain-specific annotation.
Amilcare
(Ciravegna and Wilks 2003) is a system that has been defined using (LP)2 that
is specifically designed for IE for document annotation. Amilcare has become
the basis of assisted annotation for the Semantic Web in three tools: Melita,
MnM (both developed as part of AKT – see section 7.2.1) and Ontomat (Handschuh et al. 2002).
Amilcare
has been also released to some 25 external users, including a dozen companies,
for research. It is also, as can be seen in references throughout this paper,
central to many AKT technologies and services.
7.1.2
AQUA
AQUA (Vargas-Vera et al in press) is an experimental question answering system. AQUA combines Natural Language processing (NLP), Ontologies, Logic, and Information Retrieval technologies in a uniform framework. AQUA makes intensive use of an ontology in several parts of the question answering system. The ontology is used in the refinement of the initial query, the reasoning process (a generalization/specialization process using classes and subclasses from the ontology), and in the novel similarity algorithm. The similarity algorithm, is a key feature of AQUA. It is used to find similarities between relations/concepts in the translated query and relations/concepts in the ontological structures. The similarities detected then allow the interchange of concepts or relations in a logic formula corresponding to the user query.
7.2. Annotation
Amilcare
and (LP)2 constitute the basis upon which the AKT activity on IE has
been defined. It mainly concerns annotation for the SW and KM. The SW needs
semantically-based document annotation to both
enable better document retrieval and empower semantically-aware agents. Most of
the current technology is based on human centered annotation, very often
completely manual (Handschuh et al 2002). Manual annotation is difficult, time
consuming and expensive (Ciravegna et al 2002).
Convincing
millions of users to annotate documents for the Semantic Web is difficult and
requires a world-wide action of uncertain outcome. In this framework,
annotation is meant mainly to be statically associated to (and saved within)
the documents. Static annotation associated to a document can:
(1) be incomplete or incorrect when the
creator is not skilled enough;
(2) become obsolete, i.e. not be aligned
with pages’ updates;
(3) be irrelevant for some use(r)s: a
page in a pet shop web site can be annotated with shop-related annotations, but
some users would rather prefer to find annotations related to animals.
Producing
methodologies for automatic annotation of pages therefore becomes important:
the initial annotation associated to the document loses its importance because
at any time it is possible to automatically (re)annotate the document. Also
documents do not need to contain the annotation, because it can be stored in a
separate database or ontology exactly as nowadays’ search engines do not modify
the indexed documents. In the future Semantic Web, automatic annotation systems
might become as important as indexing systems are nowadays for search engines.
Two
strands of research have been pursued for annotation: assisted semi-automatic
document annotation (mainly suitable for knowledge management) and unsupervised
annotation of large repositories (mainly suitable for the Semantic Web).
7.2.1 Assisted annotation
AKT has
developed assisted annotation tools that can be used to create an annotation
engine. They all share the same method based on adaptive IE (Amilcare). In this
sections, we will describe two tools: MnM (Vargas-Vera et al. 2002) and Melita
(Ciravegna et al. 2002) – though see also the sections on Magpie (section 7.2.2) and CS AKTive Space(section 11.1).
In both
cases annotation is ontology-based. The annotation tool is used to annotate
documents on which the IE system trains. The IE system monitors the
user-defined annotations and learns how to reproduce it by generalizing over
the seen examples. Generalization is obtained by exploiting both linguistic and
semantic information from the ontology.
MnM
focuses more on the aspect of ontology population. Melita has a greater focus
on the annotation lifecycle.
MnM
The MnM tool supports automatic, semi-automatic and manual semantic annotation of web pages. MnM allows users to select ontologies, either by connecting to an ontology server or simply through selection of the appropriate file, and then allows them to annotate a web resource by populating classes in the chosen ontology with domain specific information.
Figure 10 A Screenshot of the MnM Annotation Tool
An important aspect of MnM is the integration with
information extraction technology to support automated and semi-automated
annotation. This is particularly important as manual annotation is only
feasible in specifc contexts, such as high-value e-commerce applications and
intranets. Automated annotation is achieved through a generic plug-in mechanism,
which is independent of any particular IE tool, and which has been tested with
Amilcare. The only knowledge required for
using Amilcare in new domains is the ability of manually annotating the
information to be extracted in a training corpus. No knowledge of Human
Language technologies is necessary.
MnM supports a number of representation languages, including RDF(S), DAML+OIL and OCML. An OWL export mechanism will be developed in the near future. MnM has been released open source and can be downloaded from http://kmi.open.ac.uk/projects/akt/MnM/. This version of MnM also includes a customized version of Amilcare.
Melita
Melita is
a tool for defining ontology-based annotation tools. It uses Amilcare as active
support to annotation. The annotation process is based on a cycle that
includes:
(1) The manual definition or revision of
a draft ontology;
(2) The (assisted) annotation of a set of documents; initially the annotation is completely manual, but Amilcare runs in the background and learns how to annotate. Once Amilcare has started to learn, it preannotates every new text before Melita presents it to the user; the user must correct the system annotation; corrections (missed and wrong cases) are sent back to Amilcare for retraining.
(3) Go to 1., until the IE system has
reached a sufficient reliability in the annotation process and the annotation
service is delivered.
In this
process, users may eventually decide to try to write annotation rules
themselves either to speed up the annotation process or to help the IE system
learning (e.g. by modifying the induced grammar).
Melita
provides three centers of focus of user interaction for supporting this
lifecycle:
·
the
ontology;
·
the
corpus, both as a whole and as a collection of single documents;
·
the
annotation pattern grammar(s), either user- or system-defined.
Users can
move the focus and the methodology of interaction during the creation of the
annotation tool in a seamless way, for example moving from a focus on document
annotation (to support rule induction or to model the ontology), to rule
writing, to ontology editing (Ciravegna et al. 2003 submitted).
7.2.2 Annotation of large repositories
Armadillo
The
technology above can only be applied when the documents to be analyzed present
some regularity in terms of text types and recurrent patterns of information.
This is sometimes but not always the case
when we look at companies’ repositories. In the event that texts are very
different or highly variable in nature (e.g. on the Web), the Melita approach
is inapplicable, because it would require the annotation of very large corpora,
a task mostly unfeasible.
For this reason, AKT has developed a methodology able to learn how to annotate semantically consistent portions of the Web in a complete unsupervised way, extracting and integrating information from different sources. All the annotation is produced automatically with no user intervention apart from some corrections the users might want to perform to the system’s final or intermediate results. The methodology has been fully implemented in Armadillo, a system for unsupervised information extraction and integration from large collections of documents (http://www.aktors.org/technologies/Armadillo/) (Ciravegna et al. 2003).
The
natural application of such methodology is the Web, but very large companies’
information systems are also an option.
The key
feature of the Web exploited by the methodology is the redundancy of information. Redundancy is given
by the presence of multiple citations of the same information in different
contexts and in different superficial formats, e.g., in textual documents, in
repositories (e.g. databases or digital libraries), via agents able to
integrate different information sources, etc. From them or their output, it is
possible to extract information with different reliability. Systems such as
databases generally contain structured data and can be queried using an API. In
case the API is not available (e.g. the database has a Web front end and the
output is textual), wrappers can be induced to extract such information
(Kushmerick et al. 1997). When the information is contained in textual documents,
extracting information requires more sophisticated methodologies. There is an
obvious increasing degree of complexity in the extraction task mentioned above.
The more difficult the task, the less reliable generally the extracted
information is. For example wrapper induction systems generally reach 100% on
rigidly structured documents, while IE systems reach some 70% on free texts.
Also, the more the complexity increases, the more the amount of data needed for
training grows: wrappers can be trained with a handful of examples whereas full
IE systems may require millions of words.
In our
model, learning of complex modules is bootstrapped by using information from
simple reliable sources of information. This information is then used to
annotate documents to train more complex modules. A detailed description of the
methodology can be found in (Ciravegna et al. 2003).
Magpie
Automatic annotation could also be the key to improving strategies and information for browsing the SW. This is the intuition behind Magpie (Dzbor et al 2003). Web browsing involves two basic tasks: (i) finding the right web page and (ii) making sense of its content. A lot of research has gone into supporting the task of finding web resources, either by means of ‘standard’ information retrieval mechanisms, or by means of semantically enhanced search (Gruber 1993, Lieberman et al 2001). Less attention has been paid to the second task – supporting the interpretation of web pages. Annotation technologies allow users to associate meta-information with web resources, which can then be used to facilitate their interpretation. While such technologies provide a useful way to support group-based and shared interpretation, they are nonetheless very limited; mainly because the annotation is carried out manually. In other words, the quality of the sensemaking support depends on the willingness of stakeholders to provide annotation, and their ability to provide valuable information. This is of course even more of a problem, if a formal approach to annotation is assumed, based on semantic web technology.
Magpie follows a different approach from that used by the aforementioned annotation techniques: it automatically associates a semantic layer to a web resource, rather than relying on a manual annotation. This process relies on the availability of an ontology. Magpie offers complementary knowledge sources, which a reader can call upon to quickly gain access to any background knowledge relevant to a web resource. Magpie may be seen as a step towards a semantic web browser.
Figure 11 The Magpie Semantic Web Browser
The Magpie-mediated association between an ontology and a web resource provides an interpretative viewpoint or context over the resource in question. Indeed the overwhelming majority of web pages are created within a specific context. For example, the personal home page of an individual would have normally been created within the context of that person’s affiliation and organizational role. Some readers might be very familiar with such context, while others might not. In the latter case, the use of Magpie is especially beneficial, given that the context would be made explicit to the reader and context-specific functionalities will be provided. Because different readers show differing familiarity with the information shown in a web page and with the relevant background domain, they require different level of sensemaking support. Hence, the semantic layers in Magpie are designed with specific types of user in mind.
The semantic capabilities of Magpie are achieved by creating a semantic layer over a standard HTML web page. The layer is based on a particular ontology selected by the user, and associated semantic services. In the context of our paper, the services are defined separately from the ontology, and are loosely linked to the ontological hierarchy. This enables Magpie to provide different services depending on the type of a particular semantic entity that occurs in the text. In addition to this shallow semantics, one of the key contributions of the Magpie architecture is its ability to facilitate bi-directional communication between the client and server/service provider. This is achieved through so-called trigger services, which may feature complex reasoning using semantic entities from the user-browsed pages as data source. Triggers use ontology to recognize interesting data patterns in the discovered entities, and bring forward semantically related information to the user. The key benefit of this approach is that there may be no explicit relationship expressed in the web page – the relevance is established implicitly by consulting a particular ontology.
Magpie is an example of collaboration within AKT leading to new opportunities. One of the early collaborations within AKT (called AKT-0) combined dynamic ontologically based hyperlink technology from Southampton with the OU’s own ontology-based technologies. The final result of the AKT-0 collaboration was an extended Mozilla browser where web pages could be annotated on-the-fly with an ontology generated lexicon facilitating the invocation of knowledge services.
7.3. Identifying communities of practice
Communities of practice (COPs) are informal
self-organising groups of individuals interested in a particular practice.
Membership is not often conscious; members will typically swap war stories,
insights or advice on particular problems or tasks connected with the practice
(Wenger 1998). COPs are very important in organisations; taking on important
knowledge management functions. They act as corporate memories, transfer best practice,
provide mechanisms for situated learning, and act as foci for innovation.
Identifying COPs is often regarded as an essential first step towards understanding the knowledge resources of an organisation. COP identification is currently a resource-heavy process largely based on interviews that can be very expensive and time consuming, especially if the organisation is large or distributed.
ONTOCOPI (Ontology-based Community of Practice Identifier, http://www.aktors.org/technologies/ontocopi/) attempts to uncover COPs by applying a set of ontology network analysis techniques that examine the connectivity of instances in the knowledge base with respect to type, density, and weight of these connections (Alani et al 2003a). The advantage of using an ontology to analyse such networks is that relations have semantics or types. Hence certain relations – the ones relevant to the COP – can be favoured in the analysis process.
ONTOCOPI applies an expansion algorithm that generates the COP of a selected instance (could be any type of object, e.g. a person, a conference) by identifying the set of close instances and ranking them according to the weights of their relations. It applies a breadth-first, spreading activation search, traversing the semantic relations between instances until a defined threshold is reached. The output of ONTOCOPI is a ranked list of objects that share some features with the selected instance.
COPs are often dynamic – one typically moves in different communities as one’s working patterns, seniority, etc, change in the course of one’s career. If temporal information is available within the ontology being analysed, then ONTOCOPI can use it to present a more dynamic picture. For example, when an ontology is extended to allow representation of the start and end dates of one’s employment on a project, it is then possible to exploit that information. ONTOCOPI can be set to focus only on relationships obtained within some specified pair of dates, ignoring those that fall outside the date range.
Figure 12 The Protégé Version of the COP technology
ONTOCOPI currently exists in three different implementations; a plugin to Protégé 2000 (http://protege.stanford.edu/); an applet working with the triplestore (section 10.2); and as URI query to the 3store that returns COPs in RDF.
COP detection is an application of the basic technique of
ontology-based network analysis, and this general technique of knowledge
retrieval can play an indirect role in a number of other management processes.
In AKT, we have applied ONTOCOPI’s analysis to bootstrap other applications,
such as organisational memory (Kalfoglou et al 2002), recommender systems
(Middleton et al 2002), and ontology referential integrity management system
(Alani et al 2002 – see section 9.4).
8. Publishing
Knowledge is only effective if it is delivered in the right form, at the right place, to the right person at the right time. Knowledge publishing is the process that allows getting knowledge to the people who need it in a form that they can use. As a matter of fact, different users need to see knowledge presented and visualised in quite different ways. Research on personalised presentations has been carried out in the fields of hypermedia, natural language generation, user modelling, and human-computer interaction. The main challenges addressed in AKT in these areas were the connection of these approaches to the ontologies and reasoning services, including modelling of user preferences and perspectives on the domain.
Research on knowledge publishing in AKT has focused on three main areas:
· CS Active Space – intelligent user-friendly knowledge exploration without complex formal queries
· Artequakt – personalised summaries using story templates and adaptive hypermedia techniques
· MIAKT-NLG – natural language generation (NLG) of annotated images and personalised explanations from ontologies
CS AKTive Space (section 11.1) is an effort to address the problem of rich exploration of the domain modelled by an ontology. We use visualization and information manipulation techniques developed in a project called mSpace (schraefel et al 2003) first to give users an overview of the space itself, and then to let them manipulate this space in a way that is meaningful to them. So for instance one user may be interested in knowing what regions of the country have the highest level of funding and in what research area. Another user may be interested in who the top CS researchers are in the country. Another might be interested in who the up and comers are and whether they're all going to the same university. CS AKTive space affords just these kinds of queries through formal modelling of information representation that goes beyond simple direct queries of an ontology and into rich, layered queries (Shadbolt et al 2003).
We have mentioned Artequakt already (section 4.3),
in the context of acquisition. As far as publishing goes, the Artequakt
biography generator is based around an adaptive virtual document that is stored
in the Auld Linky contextual structure server. The virtual document acts as a
story template that contains queries back into the knowledge-base. Each query
resolves into a chunk of content, either by retrieving a whole sentence or
paragraph that contains the desired facts or by inserting facts directly from
the knowledge-base into pre-written sentence templates. It is possible to
retrieve the story template in different contexts and therefore get different
views of the structure and in this way the story can be personalised. The
contribution of the Artequakt system is in the ontological approach to the
extraction, structuring and storing of the source texts and in the use of
adaptive virtual documents as story templates.
Using whole fragments of pre-written text is surprisingly effective, as a reader is very forgiving to small inconsistencies between adjacent fragments. However, these fragments already contain elements of discourse that might be inappropriate in their new context (such as co-referencing and other textual deixis) and which can prove jarring for a reader. We are now starting to explore the use of NLG techniques for the MIAKT application (section 11.3). There are two anticipated roles; (i) taking images annotated with features from the medical ontology and generating a short natural language summary of what is in the image, (ii) taking medical reports and personalising them – for example removing technical or distressing terms so that a patient may view the records or else anonymising the report so that the information may be used in other contexts.
In addition to personalised presentation of knowledge, NLG tools are needed in knowledge publishing in order to automate the ontology documentation process. This is an important problem, because knowledge is dynamic and is updated frequently. Consequently, the accompanying documentation which is vital for the understanding and successful use of the acquired knowledge, needs to be updated in sync. The use of NLG simplifies the ontology maintenance and update tasks, so that the knowledge engineer can concentrate on the knowledge itself, because the documentation is automatically updated as the ontology changes. The NLG-based knowledge publishing tools (MIAKT-NLG) also utilise the ontology instances extracted from documents using the AKT IE approaches (see section 7.1). The dynamically generated documentation not only can include these instances, as soon as they get extracted, but it can also provide examples of their occurrence in the documents, thus facilitating users’ understanding and use of the ontology. The MIAKT-NLG tools incorporates a language generation component, a Web-based presentation service, and a powerful user-modelling framework, which is used to tailor the explanations to the user’s knowledge, task, and preferences.
The challenge for the second half of the project in NLG
for knowledge publishing is to develop tools and techniques that will enable
knowledge engineers, instead of linguists, to create and customise the
linguistic resources (e.g., domain lexicon) at the same time as they create and
edit the ontology (Bontcheva et al
2001, Bontcheva 2001).
9. Maintenance
Knowledge maintenance is key to retaining the value of a repository of knowledge (O’Hara & Shadbolt 2001); knowledge can quickly date, or become radically and immutably decontextualised by a rapidly changing environment. Inconsistencies will inevitably arise over time, particularly in an additive medium such as the WWW, where there are many more incentives to post some information than to withdraw it.
So far, AKT has focused on three major aspects of knowledge maintenance, the issues of situating knowledge with respect to changing ontologies (section 9.2), the problem of establishing and maintaining mappings between ontologies (section 9.3), and of attempting to deal with the pernicious problem of deciding when two distinct terms refer to the same object (section 9.4). AKT has also examined, in the context of information overload, the notion of forgetting. Although work here is at a preliminary stage, this is likely to become a “hotter topic” in computer science over the near future. Hence we will begin with a brief discussion of forgetting.
9.1. Forgetting
AKT researchers have been examining the notion of forgetting, in both human and machine memory, to try to understand the processes involved; although increases in computer storage capacity mean that forgetting is no longer needed for reasons of technical efficiency, as a knowledge maintenance strategy it shows much promise (O’Hara et al 2003). As external digital devices become increasingly essential for access to information, and information accumulates, demands on search engines and other retrieval systems will become even greater; in such an environment, despite the massively increased storage capacity of systems, the demands for near-instant access to information may mean that forgetting is a sensible strategy.
Of course, this intuition may be false; artful organisation of knowledge repositories may be sufficient to enable efficient search in most contexts. However, there may be a role for forgetting even with respect to such a strategy; one way of organising a repository would be to foreground information that can heuristically be tagged as being likely to be accessed, or current, and to place in the background information that may be out of date, or merely irrelevant. Such backgrounded information would then be harder to get at, as the search strategy would focus on the foregrounded information first. In a certain sense, then, the backgrounded information can be seen as “forgotten”; at any rate, the backgrounded information would clearly overlap with the information that would be genuinely forgotten under a strategy of deletion.
The human memory system has evolved in such a way that
forgetting garbage is a central strategy in its efficiency (Schacter 2001). It
therefore makes a great deal of sense to try to look at human memory as a way
of establishing useful metaphors for computing research, or indeed to try to
reconceptualise computing strategies in terms of human memory (O’Hara et al
2000, O’Hara et al 2003). For example, evidence suggests dreaming in humans
seems to facilitate the process of memorising and forgetting by allowing a
reorganisation of memory in the absence of input from perception; in certain
ways, this might be seen as analogous to garbage collection; such analogies may
help improve computer memory management.
9.2. Ontology change
As the end user’s requirements change over a period of time, the ontologies used to structure and inform knowledge assets may evolve to better suit their requirements. This gradual drift in the ontologies may lead to issues of legacy knowledge which is now being effectively expressed using a different language to that in common use.
The ConRef system (Winter et al 1998) is used to manage service or object inventories that use more than one ontology. It provides a service that transparently maps the queries expressed in the user’s ontology into the ontology used by the inventory, and allows the user to modify the mapped query by stipulating which of the returned objects are unwanted, or those objects that should have been returned. ConRef is currently being used in a prototype system with British Aerospace for managing an inventory of fasteners.
9.3. Ontology mapping
The more ontologies are being deployed on the SW, the greater the demand to share them for the benefits of knowledge sharing and semantic interoperability. The sharing of ontologies though, is not a solved problem. It has been acknowledged and researched by the knowledge engineering community for years.
One aspect of ontology sharing is to perform some sort of mapping between ontology constructs. That is, given two ontologies, one should be able to map concepts in one ontology onto those in the other. Further, research suggests that we should also be able to combine ontologies where the product of this combination will be, at the very least, the intersection of the two given ontologies. These are the dominant approaches that have been studied and applied in a variety of systems (Kalfoglou & Schorlemmer 2003a).
There are, however, some drawbacks that prevent engineers from benefiting from such systems. Firstly, the assumptions made in devising ontology mappings and in combining ontologies are not always exposed to the community and no technical details are disclosed. Secondly, the systems that perform ontology mapping are often either embedded in an integrated environment for ontology editing or are attached to a specific formalism. Thirdly, in most cases mapping and merging are based on heuristics that mostly use syntactic clues to determine correspondence or equivalence between ontology concepts, but rarely use the meaning of those concepts, i.e., their semantics. Fourthly, most, if not all approaches do not exploit ontological axioms or rules often found in formal ontologies. Finally, ontology mapping as a term has a different meaning in different contexts due to the lack of a formal account of what ontology mapping is. There is an observed lack of theory behind most of the works in this area (Kalfoglou & Schorlemmer 2003a).
Motivated by these drawbacks we have developed a method and a theory for ontology mapping and merging. The approach draws heavily on proven theoretical work but our work goes further in providing a systematic approach for ontology mapping with precise methodological steps. In particular, our method, Information-Flow based Ontology Mapping (IF-Map) (Kalfoglou & Schorlemmer 2003b), draws on the proven theoretical ground of Information Flow and channel theory (Barwise & Seligman 1997), and provides a systematic and mechanised way for deploying the approach in a distributed environment to perform ontology mapping among a variety of different ontologies.
The IF-Map system formalizes mappings of ontology constructs in terms of logic infomorphisms, the fundamental ingredient of Information Flow. These are well suited for representing the bi-directional relation of types and tokens, which corresponds to concepts and instances in the ontology realm. IF-Map is focusing on instances and how these are classified against ontology concepts. This reveals the operational semantics that the ontology’s community has chosen by virtue of how it uses its instances. The IF-Map algorithm makes use of this information in order to map onto related concepts from another ontology with which its concepts classify the same instances.
The methodological part of IF-Map consists of four major steps: (a) ontology harvesting, (b) translation, (c) infomorphism generation, and (d) display of results. In the ontology-harvesting step, ontology acquisition is performed. We use a variety of methods: use existing ontologies, download them from publicly accessible online ontology libraries, edit them in ontology editors, or harvest them from the Web (section 4.1). This versatile ontology acquisition step results in a variety of ontology language formats, ranging from KIF and Ontolingua to OCML, RDF, Prolog, and native Protégé knowledge bases. This introduces the second step, that of translation. Although there are a wide choice of translators in the public domain, we found it practical to write our own translators. We did that to have a partial translation, customised for the purposes of ontology mapping in terms of IF-Map where the only constructs needed are the concepts and a representative instance for each one of them. The next step is the main mapping mechanism: the IF-Map algorithm. We provide a Java front-end to the Prolog-written IF-Map program so that it can be accessed from the Web, and we also provide a Java API to enable external calls to IF-Map from other systems. This step will find infomorphisms, if any, between the two ontologies under examination, and in the last step of the process we display them in RDF format. Finally, we also store the results in a knowledge base for future reference and maintenance reasons.
We have used IF-Map with success in a variety of ontology mapping scenarios within and outside AKT such as mapping of computer science departments’ ontologies from AKT participating universities (Kalfoglou & Schorlemmer 2002); mapping of e-government ontologies from a case study using UK and US governments ministries (Schorlemmer & Kalfoglou 2003). We have also conducted a large-scale survey of the state-of-the-art of ontology mapping (Kalfoglou & Schorlemmer 2003a) and we are currently exploring the role of Information Flow and the IF-Map approach in the wider context of semantic interoperability and integration (Schorlemmer & Kalfoglou 2003 – submitted).
9.4. Coreference resolution
The acquisition of knowledge from heterogeneous sources by automatic means (as by the AKT harvesters as part of Hyphen – Shadbolt et al 2003) carries with it certain problems, of which unintentional coreference is a key issue; different sources may refer to the same entities by different means. If we are to be able to reason effectively with this knowledge, we need to be able to determine which entities are coreferent, and to collapse these multiple instances into a canonical representation.
We have developed tools for the identification and resolution of coreferent entities in a knowledge base (Alani et al 2002). An initial step is to perform some kind of clustering in order to create a smaller “window” over which pairwise comparisons are made in order to ascertain possible duplication. Elements in each pair in these windows are identified to be in some relation based on the attributes recorded in the ontology. These (often overlapping) clusters are then subjected to more computationally intensive methods until disjoint sets of co-referential identifiers are obtained. This is achieved by establishing that there is an equivalence relation between elements of a (possibly) coreferential set which we then quotient the set of identifiers by. The relations we build upon are comparisons of features based on attributes of entities (such as e-mail address) and distances based on string matching algorithms. We also include derived features obtained by composing ontological relations, in particular, the network analyses that underlie the community of practice identification methods (Alani et al 2003a). We have employed a number of machine learning methods to identify the clusters and performed checks for the transitivity of these identification relations in order to determine whether we obtain equivalence relations.
10. Infrastructure
Integration is a key aspect to the AKT approach, given the fissiparous tendency of any project that is based over several discrete lifecycle steps. However, the SW is a fast-moving domain for which integration via an overarching and monolithic infrastructure would be a mistake; it is important to produce lightweight infrastructures for different problem classes that can underpin opportunistic combinations of heterogeneous services – some or all of which may originate outside AKT.
AKT’s infrastructure focus has been varied through its history. In this section, we will focus on four areas: the AKTbus knowledge interchange mechanism, the AKT Triplestore, an experiment in large-scale service curation techniques, the development of Internet Reasoning Services, and Human Language Technology infrastructure.
10.1. AKTbus
The AKTbus was designed as a lightweight XML-based messaging infrastructure, to integrate a number of pre-existing systems and components from the consortium members. The AKTbus is designed to carry a variety of content languages, including simple query-response protocols such as OKBC, and agent communication languages such as KQML and FIPA-ACL. At the present time, the AKTbus infrastructure has the following components:
· an XML-based messaging protocol;
· reference implementations (fully interoperable) and APIs for Java and Prolog.
The AKTbus can be viewed as a “simpler SOAP”, as it shares many of the aims and design features of the W3C’s Simple Object Access Protocol; however, when work on AKT began, the SOAP specification was in a state of flux, and implementations were primitive and unstable. Moreover, SOAP is primarily geared to support synchronous remote procedure calls, while the AKTbus is primarily an asynchronous message-passing protocol, better-suited to communication among autonomous knowledge-driven systems, such as software agents.
This infrastructure has been used to build a number of proof-of-concept knowledge systems integrations:
specifications and implementations of FIPA and KQML agent communication languages running over the AKTbus protocol, providing a gateway to the FIPA-based Agentcities network, and the KQML-based KRAFT agent system (Hui et al, 2003);
· an AKTbus interface to the OKBC-based Protégé server;
· an AKTbus interface to Edinburgh’s I-X components, including their process panels and broker.
The AKTbus also, as we have noted, underpins the KRAFT/I-X TIE (section 6.1).
10.2. The AKT ontology and triplestore
The CS AKTiveSpace testbed (discussed further in Section 11.1) requires a dataset that describes computer science research in UK higher education. The AKT Reference Ontology was written to provide principled structure to this data, focusing on the application domain of UK academic life. Its construction has been informed by other work on the design of moderate scale ontologies, from the Open University's experiences with OCML, to external efforts such as IEEE SUO and the Cyc project.
The design and maintenance of this ontology has been a collaborative endeavour involving all AKT partners, with regular sessions at project workshops to review the performance and suitability of the ontology. The reference ontology is in many ways a living artifact, and one which we are using to explore the issues of ontology evolution and maintenance. To ensure maximal interoperability with the different tools in use in the consortium, the ontology is maintained in five different languages of varying degrees of expressivity: OCML, OntoLingua, DAML+OIL, OWL and RDF Schema.
The AKT triple
store was developed to provide a storage and query interface to the large
volumes of RDF data that the project requires to research the possibilities of
a semantically described domain. The triple store was designed to take
advantage of established relational database technologies and techniques to
allow for efficient access to the RDF data and entailments. The principles
behind this technique have been described in (Harris and Gibbins 2003). The
software, 3store, has been tested on knowledge bases with up to 25 million RDF
triples and is expected to scale to much larger volumes, which makes it one of
the most scalable generic RDF storage technologies currently available. It has
been released under a Free Software licence to enable its use in other research
projects and by commercial entities and has been downloaded by around 70
individuals and projects. A number of AKT systems have been connected to the
triple store such as the I-X Process Panels and on a larger scale the CS AKTive
Space IFD described in Section 11.1.
The AKT triple-store system (3store) is written in C for POSIX compliant systems, and so is portable to most UNIXes. It uses MySQL as its back-end repository, storing RDF triples in an ontology neutral database schema. It is available from SourceForge at http://www.sourceforge.net/projects/threestore/. RDFS entailments are generated using a hybrid eager/lazy approach (Harris & Gibbins 2003), where queries are adaptively translated into SQL database queries and executed by the RDBMS engine. This allows for very efficient queries (typically a few milliseconds) over large knowledge bases.
10.3. Internet Reasoning Services
As we have argued, SW services hold enormous potential. The augmentation of web services with formal descriptions of their competence will facilitate their automatic location, mediation, and composition. The IRS-II (Internet Reasoning Service) is a framework and implemented infrastructure to support the publication, location, composition and execution of heterogeneous web services, augmented with semantic descriptions of their functionalities (Motta et al 2003). IRS-II has three main classes of features which distinguish it from other work on semantic web services. Firstly, by automatically creating wrappers, standalone software (we currently support Java and Lisp) can be published through the IRS-II very easily. Secondly, because IRS-II is built on a knowledge modelling framework, we provide support for capability-driven service invocation, for flexible mappings between services and problem specifications and we support dynamic, knowledge-based service selection. Finally, IRS-II services are web service compatible – standard web services can be trivially published through the IRS-II and any IRS-II service automatically appears as a standard web service to other web service infrastructures.
The approach taken in IRS-II is based on the UPML framework (Fensel and Motta, 2001), one of the main results of the EU funded IBROW project (Benjamins et al., 1998). The UPML framework partitions knowledge into domain models, task models, and problem solving methods each supported by appropriate ontologies. Domain models capture the essential concepts and relationships in a domain. Task models contain declarative representations of capabilities, specifically, the goal of a task, the types of inputs and outputs, and pre-conditions. Problem solving methods can be thought of as knowledge level descriptions of generic reasoners which can be harnessed to solve tasks. One of our main contributions in this work has been to integrate the UPML framework with web services.
10.4. Human language technology infrastructure
Human Language Technology (HLT) plays an increasingly important role in KM in the context of the Semantic Web. With the expansion of KM, and the application of knowledge technologies, to large-scale tasks in the SW or corporate intranets, so the need for robust, large-scale HLT is increased. Our work in this area focuses on supporting experimental repeatability and quantitative measurement, within an open source environment, GATE (Cunningham et al 2002), engineered to an exceptional standard. GATE has thousands of users at hundreds of sites.
As we have seen, ontologies and reasoning abilities are necessary components across systems to exploit the possibilities of the SW, certainly for most HLT applications. Such ontologies may be created by people or applications independent of the HLT context; perhaps by merging old ontologies, or by automatic generation of ontologies from some legacy sources. Since such ontologies may be created independently of an HLT application, their suitability may be marginal; and given the lack of standards it is likely that many ontologies will contain hard-to-detect errors. HLT components – which often do not support ontologies and reasoning as explicitly reusable components – will often need to exchange knowledge with other services and technologies that provide non-HLT services.
Hence there are clear opportunities for HLT infrastructures within AKT to exploit. There are requirements for HLT infrastructures that support ontologies and reasoning, particularly supporting existing ontology standards such as DAML+OIL. Ontology maintenance and use should become part of the HLT application building process, and ontology-aware HLT components should become capable of sharing and exchanging ontologies with other tools, including non-HLT tools.
To this end, AKT has extended the world-leading GATE infrastructure to support SW-enabled HLT, providing interoperability with existing SW tools and other knowledge technologies (including from within the AKT consortium, of course) via standards such as RDF and DAML+OIL. Integration of linguistic data and knowledge is being achieved via ontologies, and there is support for the creation of ontology-aware HLT modules. Ontology API allows unified access by HLT components, regardless of the original ontology format, making it easier for components to exchange ontologies, following a similar route to the exchange of standard linguistic data (e.g. lexicons). AKT HLT infrastructures now allow integration with knowledge technologies, and therefore their creative reuse; for example, Protégé has been used for ontology visualisation and maintenance.
10.5. Collaborative Work Environments
We have invested significant effort in integrating existing and developing new tools for collaborative work. Moreover, as demonstrated in the Management Report we have used these tools for organising and supporting our own management and work processes.
10.5.1 CoAKTinG Collaborative Technologies
The CoAKTinG testbed (Collaborative Advanced Knowledge Technologies in the Grid: www.aktors.org/coakting) is extending and integrating AKT technologies specifically to support distributed scientific collaboration – both synchronous and asynchronous – over the Grid and standard internet. Now halfway through, the CoAKTinG testbed has integrated technologies for instant messaging/presence awareness (OU), real time decision rationale and group memory capture (OU), issue handling and coordination support (Edinburgh), and semantically annotated audio-visual streams (Southampton). See the summary in Table 2; details of the approaches plus an extended use scenario can be found in Buckingham Shum, et al. (2002).
Pair-wise integrations between the above tools has been demonstrated, while an example of multi-way integration is the meeting navigation and replay tool (Bachler, et al., 2003) illustrated in Figure 13, which integrates metadata grounded in a meeting ontology for scientific collaboration, with time-based metadata indicating current slide, speaker, and issue under discussion, to enable novel forms of meeting navigation and replay.
Figure 13: CoAKTinG proof of concept web interface for navigating an AV meeting record by time, speaker, slide, and issue under discussion. The two coloured bars indicate slide transitions (top) and speaker (bottom). The current speaker at the time selected is indicated at the foot, plus an indication of the current issue under discussion, extracted from the Compendium record (Bachler, et al., 2003).
CoAKTinG Collaboration
Technologies
Ontologically annotated
audio/video streams for meeting navigation and replay. Few
researchers have the time to sit and watch videos of meetings; an AV record of
an online meeting is thus only as useful as its indexing. Moreover, indexing
effort must negotiate the cost/benefit tradeoff or it will not be done. Prior
work has developed ways to embed ‘continuous metadata’ (of which one form is
hyperlinks) in media streams. Within CoAKTinG, this work forms the platform for
integrating multiple metadata streams with AV meeting records (as illustrated
in the meeting replay tool).
Issue handling, tasking,
planning and coordination. We are building applications using I-X Intelligent
Process Panels and their underlying <I-N-C-A> (Issues, Nodes, Constraints
and Annotations) constraint-based ontology for processes and products [i-x.info]. The process panels provide a
simple interface that acts as an intelligent “to do” list that is based on the
handling of issues, the performance of activity or the addition of constraints.
It also supports semantically task directed “augmented” messaging and reporting
between panel users. A common ontology of processes and process or
collaboration products based on constraints on the collaborative activity or on
the alternative products being created via the collaboration is the heart of
this research.
Collective sensemaking and
group memory capture. Whilst meetings are a pervasive knowledge-based
activity in scientific life, they are also one of the hardest to do well.
“Meeting technologies” tend either to over-structure meetings (e.g. Group
Decision Support Systems), or ignore process altogether, and simply digitize
physical media (e.g. whiteboards) for capturing the products of discussion. The
Compendium approach [www.CompendiumInstitute.org] occupies the hybrid middle-ground – ‘lightweight’
discussion structuring and mediation plus idea capture, with import and export
to other document types. Dialogue maps are created on the fly in meetings
providing a visual trace of issues, ideas, arguments and decisions.
Enhanced presence management and visualisation. The concept of
presence has moved beyond the
‘online/offline/away/busy/do-not-disturb’ set of simple state indicators
towards a rich blend of attributes that can be used to characterise an
individual's physical and/or spatial location, work trajectory, time frame of
reference, mood, goals, and intentions. Our challenge is how best to
characterise presence, how to make it easy to manage and easy to visualise, and
how to remain consistent with the user’s own expectations, work habits, and existing
patterns of Instant Messaging (IM) and other communication tool usage. Working with
the Jabber open source XML-based communications architecture, we have released
a prototype called BuddySpace which overlays presence
information onto visualisations, both geographical (e.g. a map of a building,
or a region), and conceptual (e.g. a workflow chart or project plan, a design
or experiment). [kmi.open.ac.uk/projects/buddyspace]
Table 2: CoAKTinG Collaboration Technologies
10.5.2 Use Case: Building the AKT Reference Ontology
An example of our use of our own collaborative work environments can be seen in the production of the AKT reference ontology is shown in Figure 14. The life-cycle consists of a pre-meeting, a live meeting and a post-meeting stage. The final outcome is the AKT Reference ontology published in a variety of formats and representation languages.
Figure 14 The life-cycle used to develop the AKT reference ontology
In the pre-meeting stage a proposal document written by a researcher at Edinburgh was automatically transformed into a Compendium map. The headings were turned into questions and the first line of each paragraph was turned into a statement.
The setup for the live meeting is shown in Figure 15. Participants sat around a table in a room containing two screens. The first displayed candidate ontology definitions held on various ontology servers. As the meeting progressed the discussion was captured in a Compendium map – see the screen snapshot in the bottom right of Figure 14.
Figure 15 Room Layout for AKT Reference Ontology Meeting
Once the meeting had finished the Compendium map was exported both as a series of images (see the screen snapshot on the middle far left of Figure 14) and as a HTML page (the screen snapshot on the middle of Figure 14). Using D3E (see http://www.aktors.org/technologies/D3E - Digital Document Discourse Environment/) the exported maps were integrated into a threaded discussion space. The D3E toolkit is able to automatically create and link discussion threads to plain HTML documents. New comments were automatically sent to an AKT Reference Ontology mailing list.
Using the discussion site the main issues raised in the meeting were considered for a further month. At the end of the month an initial version of the ontology was codified in OCML (Motta, 1999) and published using the D3E toolkit. Once all comments on the initial version were collected a second was created and released in a variety of formats (see Figure 14 bottom left) including OCML (as a hyperlinked set of web pages and on the WebOnto server), Ontolingua (a hyperlinked set of web pages), DAML+OIL and OWL. All of these versions are available from www.aktors.org/publications/ontology/.
There are two main lessons can we draw from our experiences of technology support of collaborative work within AKT. Firstly, a blended model of collaboration using a variety of technologies is required to support technical work. At the start of a collaborative venture, whilst the specific problem is being framed face-to-face meetings are necessary. Later on synchronous virtual meetings and asynchronous communication, via D3E and email, can be used to co-ordinate evolutionary technical development. From time-to-time further face-to-face meetings are required to re-frame the problem or to unify significant developments. This model bears some resemblance to Fischer’s Seeding Evolutionary Growth and Reseeding model (Fischer et al., 1994) for the incremental development of design systems.
The second lesson is based on the way documents are used within organisations. Rather than serving as a means to objectively transmit knowledge the key role of documents within organisational settings is to support negotiation and interpretation as communities struggle to reach a shared understanding. Building on the OU’s Enrich methodology and Southampton’s rich linking technology (Motta et al., 2000; Carr et al 2001), the key to sharing knowledge within AKT has been to support the enrichment of our collaborative work representations.
11. Applications
The AKT concept was always based strongly around the idea of testbeds. Such testbeds – real-world contexts for the application of knowledge services and technologies – are important not only for robust proof of concept, but also to aid the process of integration of the AKT components and the transfer of results to the user community. We present three under development at present.
11.1. CS AKTive Space
CS AKTive Space attempts to provide an overview of current UK University based research in Computer Science. The application exploits a wide range of semantically heterogeneous and distributed content relating to Computer Science research in the UK. It provides services such as browsing topics and institutions for researchers, it can show the geographic range and extent of where a topic is researched, provides an estimation of “top” researchers in a topic and by geographic region, is able to calculate a researcher’s Community of Practice. We chose this area for a number of reasons; (i) we had a real interest in having such a set of services, (ii) it is a domain that we understand, (iii) it is relatively accessible and easy to communicate as a domain, (iv) we were able to secure access to a wide range of content that was not subject to industrial embargo, (v) it presented real challenges of scale and scope.
The application exploits a wide range of semantically heterogeneous and distributed content relating to Computer Science research in the UK. For example, there are almost 2000 research active Computer Science faculty, there are 24,000 research projects represented, many thousands of papers, hundreds of distinct research groups.
This content is gathered on a continuous basis using a variety of methods including harvesting and scraping (Leonard and Glaser 2001) as well as other models for content acquisition. The content currently comprises around seven million RDF triples and we have developed storage, retrieval and maintenance methods to support its management (Harris and Gibbins, 2003). The content is mediated through an ontology (http://www.aktors.org/publications/ontology/) constructed for the application domain and incorporates components from other published ontologies (Niles and Pease, 2001).
CS AKTive Space supports the exploration of patterns and implications inherent in the content. It exploits a variety of visualisations and multi dimensional representations that are designed to make content exploration, navigation and appreciation direct and intuitive (schraefel et al 2003). As mentioned the knowledge services supported in the application include investigating communities of practice (Alani et al 2003a) and scholarly impact (Kampa 2002).
We aim to provide a content space in which a user can rapidly get a Gestalt of who is doing what and where, what are the significant areas of effort both in terms of topic and institutional location, what of this work is having an impact or influencing others and where are the gaps in research coverage. In Figure 16 we see a screenshot of CS AKTive Space. We can see that on the right a region has been selected in the middle of the country by dragging a reticule of selected radius over it. This action will have dispatched a complex query to our underlying RDF repository the results being all the Computer Science Departments in that region and the corresponding topics that they research according to a taxonomy of research taken from the ACM. In the case of Figure 16 we have selected artificial intelligence as the topic of interest and a further compound query will have been sent to return in this case the top 5 researchers in the area. This is currently determined by impact factors such as the size of their grant portfolio but we will be making this much more customisable in future versions.
Figure 16 CS AKTive Space: A Semantic Web Application for UK Computer Science Research
Finally one of the researchers has been selected Goble and her community of practice has been calculated – showing us those researchers with whom she has strong links in terms of relations such as co-authoring, co-investigator and so on.
This work illustrates a number of substantial challenges for the Semantic Web. There are issues to do with how to best sustain an acquisition and harvesting activity. There are decisions about how best to model the harvested content; how to cope with the fact that there are bound to be large numbers of duplicate items that need to be recognised as referring to the same objects or referents; the degree to which our inferential services can cope as more content becomes available; how we present the content so that inherent patterns and trends can be directly discerned must be considered; how trustworthy is the provenance and accuracy of the content; and how all this information is to be maintained and sustained as a social and community exercise.
Finally, it is worth making a brief note here about our approach to visualisation of research issues. The key activity of a researcher is research: the systematic investigation of a corpus of literature. Digital libraries provide integrated browse-and-search access to large collections of scientific and technical literature, but many of the the questions which researchers bring to a digital library (such as “What other papers did this project publish?” or “What are the significant research groups in this field?”) are situated in the knowledge domain and not supported by current Web environments (Kampa 2002).
By applying (Figure 17) the AKT ontology and triple store to Southampton’s existing OpCit bibliographic analysis environment (Hitchcock et al 2002), we have produced an infrastructure that offers a portfolio of knowledge services that improve digital library browsing by offering community analyses (Figure 18) as well as co-citation visualisation (Figure 19). To increase the range of visualisation types available, an independent geographic visualisation interface to the Triple Store was developed (Figure 20). This, as we have seen (Figure 16), subsequently became part of the CS AKTive Portal.
Figure 17: Application of AKT infrastrcture to OpCit
Figure 18: Community analysis
Figure 19: Co-citation network
Figure 20: Geographic visualisation
11.2. The Rolls-Royce Test Beds
Two of the main testbeds for AKT in the first half of its funding were provided by Rolls-Royce. Rolls-Royce takes seriously the need to keep accurate records of design, manufacture and testing; in part this is due to the nature of their business (aerospace) and in part due to the recognition that their main method of capturing corporate memory is through such documentation. As with many organisations, information systems at Rolls-Royce have evolved over time, resulting in a number of document and information management systems. In addition, like many companies in the early 1990s Rolls-Royce outsourced the management of their IT systems. This has greatly influenced the scope of the two testbeds, as it is not possible for AKT to quickly integrate prototype systems for investigating alternative designs, or to carry out software trials. As a result AKT has focussed on using Rolls-Royce data and providing demonstrators (the Intelligent Document Retrieval system and the Designers’ Workbench) to show how advanced knowledge technologies can be applied in an advanced international manufacturing company.
11.2.1 Intelligent Document Retrieval (IDR) demonstrator
Within Rolls-Royce Each Operational Business Unit (OBU) is staffed with a number of engineers from different specialisations, as components are usually designed by a number of specialists thereby creating federated (multi-perspective) views. To facilitate easier dissemination of knowledge within a given OBU, each OBU has developed its own, largely hand crafted, website.
In the first phase of this testbed we carried out a number of Knowledge Acquisition interviews with selected engineers, from whom simple ontologies of concepts and relationships were derived. During these interviews the ‘card sort’ technique (Shadbolt and Burton, 1995) was used to elicit how the engineer referenced different document types, and the relationships between these documents (see Figure 21). A demonstrator was built that showed how, by using a simple ontology, appropriate documents can be retrieved from the document repository. The demonstrator used techniques developed from:
· An ontological hypertext system Conceptual Open Hypermedia Services Environment (COHSE – Carr et al 2001);
· A framework for developing ontologically driven portals (Ontoportal – Kampa et al 2001) which allows different ontologies to be used on the same document set.
Depending on the function (design, stress, thermodynamics
or manufacturing) the engineer is undertaking, he/she is presented with
appropriate concepts from the ontology. By selecting a concept, appropriate
documents are returned; these are ranked based on the document types the
engineers most commonly used to undertake the task.
Figure 21: A typical result of a card sort
During this phase, AKT
researchers noted that Rolls-Royce were capturing knowledge through hand crafted
OBU specific websites, where information can easily be lost (e.g., links
removed). Hence, future work will include the development of tools and
methods to enable engineers to create semantically enriched meta-data, to
facilitate the capture, reuse and maintenance of this knowledge. Additionally, we plan to evaluate
the benefits of an Intelligent Document Retrieval system against standard
Information Retrieval metrics.
11.2.2 The Designers’ Workbench
The
primary aim of the Designers’ Workbench is to assist designers by checking
designs for violations of constraints. These constraints are often in the form
of simple, easy to overlook, rules. This will allow the user to focus on more
important issues. The Workbench also stores the rationales for the constraints,
so that new designers can learn why the rules are used, and so that experienced
designers can see if a rule is obsolete, and requires modification.
In the
current version of the Workbench, the user can select a feature from an
ontology, and use it to annotate an existing drawing. Each type of feature has
its own set of properties, and values can be assigned to some or all of the
properties of each feature. At any stage, the user can check the constraints.
The system will search the features to see which of them (singly or in
combination) are affected by constraints. If any constraints are applicable,
they are checked, and any violations are reported. Changes to the design or to
the constraints can be made so as to remove the violations. The features are
represented using RDF, and the search for features affected by a constraint is
performed using RDQL. The actual checking of the constraints is done by calls
to Sicstus Prolog predicates.
Figure 22: The Designers’ Workbench interface
Planned
future work on the Workbench includes:
·
Integration
with existing CAD and knowledge-based engineering systems.
·
Recording
the decisions that were made during design as unobtrusively as possible.
·
Generation
of reports from the stored design knowledge, saving work for the designer.
·
Intelligent
Editors which extract important information from documents semi-automatically
·
Case
based retrieval of previous, similar, designs
· Tools to allow engineers to capture and maintain their own knowledge bases.
11.3. MIAKT: Supporting Medical Decision Making
Clinical diagnosis and treatment of disease are
undergoing a transformation. Clinicians are increasingly required to integrate
images and signals of different types, at spatial scales that range from
microns (e.g. cells) to mm (MRI) to several cm (EEG), and on temporal scales
ranging from microseconds to months. As a result, clinicians are experiencing a
deluge of data, arising mainly from images and signals. But this deluge poses
as many problems as opportunities for the busy clinician. In fact, clinicians
need information not data, where information comprises data plus interpretation
for clinical relevance.
The project ‘MIAKT Grid enabled knowledge
services: collaborative problem solving environments in medical informatics’ www.aktors.org/miakt is a joint initiative between the
AKT IRC, specialising in knowledge technologies for the management and
synthesis of appropriate information and knowledge content, and the MIAS IRC,
specialising in the intelligent analysis and handling of medical data. This 24
month project was awarded to the AKT IRC as part of the UK e-Science programme
and started in the Summer of 2002. The aim of the project is to apply the
capabilities of AKT and MIAS to collaborative medical problem solving using
knowledge services provided via the e-Science Grid infrastructure.
The initial focus of the project is the Triple
Assessment (TA) method in symptomatic focal breast disease. The domain has been
chosen because it contains a number of characteristics that make it especially
valuable for the application of knowledge technologies and image analysis.
These characteristics include:
· Large amounts of complex data,
information and knowledge necessary to inform decision making
· Computationally intensive image and
signal interpretation problems
· The collaborative and distributed
nature of the task
· Little current support for the
knowledge based management of content.
The AKT part of the project is focusing on
developing the following:
Ontology Services – We have developed a Triple
Assessment (TA) ontology covering concepts and processes in the TA process
(Wilson et al 2001, Hu et al, 2003). This includes different stakeholder
ontologies, ontologies from different imaging methods (in particular, X-ray
mammography and MRI) and ontologies from different aspects of the TA process
(in particular radiology and histopathology). Mappings between these ontologies
are being developed and maintained. This work has made use of substantial amounts
of technology and experience derived from the core AKT project.
Figure 23 MIAKT Tool: Ontology Annotation with Feature
Detection
A set of tools and services are being developed
to provide for retrieval of image sets, linking of content, automatic and
semi-automatic annotation of content and personalization of content based on
individual stakeholder ontologies. We have already experimented with different
architectures so that the various modalities of storage, analysis and retrieval
can be accommodated (Figure 23). In particular, the
3store developed as part of the core AKT infrastructure is now part of the
current architecture. Among other facilities we seek to provide is that of
generating natural language summaries of annotated images. Requirements for
these tools are being developed with the medical experts as well as image
processing specialists from MIAS.
As well as ontology markup the enrichment we
are investigating the inclusion of discussion threads, and decision rationales
that reflect how the experts have come to particular conclusions.
The other
aspects of this project that are principally the responsibility of the MIAS IRC
partners includes services invoked over the grid to enact dynamic registration
of images (e.g. aligning images from one patient visit to the next), dynamic
construction of fused images. Finally, we are using the IRS architecture (Motta
et al 2003, and section 10.3) as a way of integrate web and grid services.
12. The science of semantic web services
AKT, as noted in section 3, is organised around the basic challenges for knowledge management. However, in the complex, distributed world of the Semantic Web, where the problems and contexts will be highly heterogeneous, a similarly heterogeneous set of tools and approaches would be required for complete coverage. Obviously, complete coverage, if not exactly a pipedream, is well beyond the scope even of a large interdisciplinary IRC such as AKT. But AKT, in facing the demands of the likely SW context, has needed to develop many heterogeneous services and technologies, integrated by a unified approach to KM, a single understanding of the likely course of development of the SW, common infrastructure assumptions, and experience of real-world problems as exemplified by the testbeds.
Hence there are various themes that can be detected running through AKT through all the challenges, and which could easily be seen as further organising principles for the project. As well as obvious issues such as the creation, population, use, reuse and visualisation of ontologies, both to structure knowledge repositories and acquisition efforts, and to act as protocols for communication with other services and technologies, a number of interesting questions have been detectable throughout the range of AKT’s research across all the challenges (Figure 24). Indeed, many of these issues have presented themselves as pressing precisely because they turned out to influence much of AKT’s research, rather than because they were anticipated in advance.
Figure 24: Issues for semantic web services
These issues include:
· Human factors. Technologies and services cannot be the complete solution to any problems, though they will be 50% of a good solution. They must be accepted and appropriate within the organisational context, and for example the production of ontologies will clearly be eased if the ultimate users have the ownership of the development process (Domingue et al 2001).
Indeed, this conception of the importance of the human dimension has always been a key part of the SW (Figure 25). In the well-known diagram of the levels of the SW, the top level is that of trust. AKT has already been examining theories of trust of both technology and processes, and it is anticipated that this will be a key area for research in the project (O’Hara in press).
Figure 25: The levels of the Semantic Web
· Service coordination. It is likely that different services will be available from many heterogeneous sources, and efficient use of them will depend on the ability to broker services, and to understand how best to compose services to produce the desired outcome. AKT has addressed these issues under many headings, and answers to these questions would add greatly to the power of the AKT approach (cf sections 5.1, 5.2, 6.2, 7.2.2, 10.2 etc).
· Service specification. Similarly, finding languages for specifying services, coordinating ontologies for sharing understanding, will be a further important set of research issues. We are actively engaged in work to define DAML-S which aims to become a widely adoped Semantic Web Services Language.
· Legacy management. The WWW contains a great deal of material, much of which remains useful for a hard-to-specify quantity of time. Managing this material, gaining the maximal amount of leverage from it, will be crucial for optimising the value of the SW. Put more negatively, it is also essential that the smooth functioning of the SW is not gummed up by the amount of dross that remains on the WWW. In particular, issues to be dealt with include the use of out-of-date formalisms, and the large amount of knowledge – often highly useful knowledge – that is buried in natural language. Clearly AKT has been deeply involved with these issues, and will continue to be so (cf sections 4.2, 4.3, 7.1, 7.2, 9, 10.4 etc).
· Distributed computation. Include problems of scale and inferential precision/completeness
These issues will form part of the research agenda for anyone working to build the next generation of intelligent web services. We anticipate that they will be important forcing functions on our own work and they figure in the explicit work we anticipate for the next phase of AKT.
13. Future work
Objectives for the full term of the IRC remain largely coincident with the seven originally stated.
· To provide an integrated set of technologies, methods and services to support the management of knowledge through its entire life cycle from acquisition to maintenance and obsolescence.
However, this is now fully understood to be in the context of web services and knowledge content on the web
· To apply these technologies to various real-world testbeds that raise a series of problems at all stages of the evolution of knowledge, and thereby demonstrate proof of concept.
We anticipate that this will involve an extension of our work with Rolls Royce. We also envisage that our current variety knowledge management work in the context of e-Science will provide further real application contexts. Finally, we anticipate continued use of our technologies in the context of our own scientific knowledge management.
· To increase the availability and ubiquity of knowledge technologies, e.g. by integrating them with web browsing technology.
The world has moved this way anyway – we are and must continue to do this. Associated with this is a likely move to adopt a more thoroughgoing agent-based approach to our services.
· To develop a robust infrastructure to handle knowledge from a wide range of sources, and to automate translation from context to context as far as possible.
The role of ontologies are proving crucial in this regard as are web services capable of various transformation and mapping functions.
· To increase the range of knowledge that can be acquired, modelled and managed, e.g. by developing techniques to acquire temporal knowledge, process knowledge, or knowledge from natural language texts.
We have a range of natural language research that deals with the acquisition of knowledge from text. We are also heavily engaged in dealing with knowledge held in non-textual formats such as images and video. The issue of acquiring knowledge that relates to temporal or process oriented content will need to be addressed especially as it related to workflow and service configuration,
We expect work on the formulation
and evaluation of user interfaces to become a more significant aspect of work
within AKT.
· To increase the value of knowledge as an asset by enabling the retrieval and reuse of knowledge.
All of our work on information
extraction and annotation is addressing this issue and we expect it to remain a
key activity.
· To
facilitate knowledge sharing by developing technologies for the automatic
publishing of knowledge in a form customised for its intended user.
Whilst we have provided support
for general sorts of customisation for classes of user there has been less work
on personal customisation in the first phase except of our work on recommender
systems (Middleton et al, 2002). However, we do expect this to change as we
look at how information spaces could be rapidly customised to support individual
interests and views.
What we have learnt in the course of the first three years
is just how far these objectives require us to confront very significant
research challenges that lie at the heart of computer science.
These include (i) the construction and maintenance of
multiple ontologies, (ii) at what grain size to construct effective ontologies
and how task specific or neutral to make them (iii) the continued challenge of
capturing and annotating content on a web scale, (iv) determining the provenance
of and establishing trust in knowledge content and annotations on the web (v)
managing and dealing with meaning equivalence on the web (when do two
descriptions refer to the same object or two sentences mean the same thing),
(vi) constructing inference services that are robust in the face of
inconsistent and incomplete information, (vii) developing a rich computational
notion of context.
An issue that we have focused on more than anticipated in
the first phase and that will continue to be important is active task and
collaboration support in making use of knowledge in support of organisational
and individual objectives.
Undoubtedly we will continue to develop our research so
that it can both be exploited and informed by grid based computing. Another
general extension of our work will be to ambient intelligence domains where
computing services become more pervasive and ubiquitous and the boundary
between the physical and digital continues to blue. We expect in this regard to
deepen and extend our interaction with the Equator IRC.
We have already mentioned our involvement in the UK OST Foresight Programmes in Cognitive Systems and Cybertrust. In the first of these we envisage developing a stream of work to address the Memories for Life Grand Challenge. In the case of Cybertrust we expect this to become a topic of real urgency as individuals and organisations realise how important and hard it is to build effective computational models of provenance and trust (O’Hara in press, Chapter 5).
14. Conclusion
The original aim of AKT was to provide “joined up” forms of knowledge technologies appropriate to the (then anticipated, now real) challenges of managing knowledge in the large-scale distributed setting afforded by the internet, and to develop the theories underpinning these new technologies. We classified our research into six themes corresponding to typical traditional stages in the lifecycles of such systems (acquisition, modelling, re-use, retrieval, publishing and maintenance) – the idea being to innovate in each of these stages through interactions between them.
Sections 4 to 9 of this document describe progress according to this classification. Our work on acquisition has focused on the harvesting of ontologies from unstructured or semi-structured sources; the key contributions being in the improvement of accuracy in harvesters by exploiting the benefits of large scale and heterogeneity in corpora (these traditionally being viewed as a problem rather than an opportunity) and assessing the leverage gained by provision of minimal ontological backbones. Rather than considering narrowly the modelling of problem solvers or knowledge bases, our modelling efforts tackle issues across the lifecycle – the most obvious of these being to model lifecycles themselves and the coordination between Web services, but we have also gained insights into essential modelling processes such as the mapping and merging of ontologies. Related to this is our research into reuse of Web services via brokering systems and our experiments in mediating between problem solvers via partially shared ontologies. As the Semantic Web evolves from the traditional Web we are devising retrieval mechanisms that scale to the task of annotating large volumes of semi-structured legacy material and that use such annotations to provide flexible query answering and semantic browsing of Web sites. While our retrieval work has concentrated on the transition from informal to formal media, our work on knowledge publishing demonstrates how formally expressed knowledge may be made more personal by applying human-centred means of interpretation – for example layered querying, template-based personalisation or natural language generation. Our maintenance tools also respond to the human problems associated with inevitable “ontology drift” as use of language in an organisation changes over time – our coreference, mapping and merging systems providing assistance in managing this drift.
Although our themed classification above has provided hooks upon which to hang specific contributions of our research it is not of itself our driving force for innovation. That is provided by the need to provide large scale support of knowledge management in distributed environments. This force has increased considerably since the outset of AKT, thanks to the Semantic Web along with related computation grid and ambient intelligence initiatives. A beneficial consequence is that we now have natural foci for integration of AKT efforts (for example in supporting the provision of Web services) and we are more likely to understand our research in terms of such foci, and the issues they raise, rather than the more traditional classification with which we began. This need not change the nature of the fundamental science but it does change the way we may think about it and connect it to the broader research community. Specific examples of the hot topics now evolving in this new context are:
· Construction and maintenance of multiple ontologies, since Web services may be designed independently but must interact (with clients and with one another). There will be practical issues of grain size and task specificity in addition to the technical issues of legacy management, mapping and merging with which AKT is familiar.
· Managing the evolution from Web to Semantic Web through annotation on a large scale. Automated annotation is sensitive to the nature of the material being annotated – small, rigidly structured texts being capable of much more precise automated annotation than large, free texts for example. This is a spur for empirical analysis and further development of AKT annotation tools.
· Maintaining semantic coherence of as high a degree as reasonably possible between Web services. Semantic equivalence cannot be guaranteed in this sort of open environment so we need ways of managing equivalence of meaning between services – an issue that is pertinent to a range of existing AKT results, from coreference resolution to coordination protocols. We also need ways of building and coordinating services that are robust in the face of inconsistent or incomplete information.
· Specifying services in ways that allow them to be easily described and coordinated. Emerging standards (such as DAML-S for service specification) provide a point of reference but do not in themselves answer the problem of how Web service specifications are most effectively harnessed for essential tasks such as brokering and they do not explain how services should be composed or coordinated. Here AKT can apply its experience in construction of composite services by integrating components; of coordinating groups of services with intersecting ontologies; and of specifying re-usable coordination protocols.
· Determining the provenance of and establishing trust in knowledge content and annotations on the web. This depends on developing a rich computational notion of context that also is practical in use. Perhaps more than most of the other issues above this cuts across a broad range of existing AKT work, from human-centred research into communities of practice to the formal methods used for propagation of properties in our lifecycle calculus. It also draws on the experience gained from our testbed projects.
To tackle problems like those above it is necessary to have gained momentum in relevant theory and technology; to have put in place substantial infrastructure capable of supporting controlled experimentation; and to have the group coherence necessary to focus on areas where AKT can make a difference. This has required substantial investment from all the AKT participants: re-interpreting traditional theory in a new context; devising and adapting technologies to suit shared research goals; building knowledge bases and infrastructure for empirical experiments; and fostering the human contacts which support a common research culture. It has, however, left us well placed to confront the challenges described above.
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