Although we often suffer from an overdose of data, all too often the problem is that the knowledge available is insufficient or poorly-specified. The challenge here is to get hold of the information that is around, and turn it into knowledge by making it usable. This might involve, for instance, making tacit knowledge explicit, identifying gaps in the knowledge already held, acquiring and integrating knowledge from multiple sources (e.g. different experts, or distributed sources on the WWW), acquiring knowledge from unstructured media (e.g. natural language or diagrams).

Knowledge acquisition (KA) is a field which has reached a certain level of maturity. It began as part of the drive to build knowledge-based systems, and was a line of research devoted to developing methods and software tools to provide knowledge content for such systems. There are many tools and techniques available, and a number of integrated workbenches and methodologies on the market. It is not the intention of AKT to reinvent this particular wheel. However, where AKT can make a difference is in a number of tricky, non-mainstream areas of knowledge representation from which the extraction or acquisition of knowledge is non-trivial or poorly-understood.

Ontologies, or sharable conceptualisations of domains, are a central technology for knowledge management (KM). However, ontology construction is a considerable overhead on any KM programme. AKT is investigating generating ontologies automatically from text, using text mining and natural language techniques. Such technology is expected to be very important given that much knowledge in science and industry is kept in informal natural language repositories. AKT is expected to make progress in the integration of natural language information extraction (IE) techniques with standard KA methods, for example applying domain ontologies to facilitate the IE from texts.

Other areas of interest to AKT are: artefact enrichment, in other words, methods, languages and logics for annotating and enriching the content of objects such as web pages, KA materials, hypertext fragments etc.; multimedia KA, i.e. coming to understand (finding models and structures for) knowledge expressed in non-text media; and incidental KA, the acquisition of knowledge as a by-product of other processes. For instance, software to facilitate meetings and collaboration can also help with integrating multiple perspectives and the construction of a collective memory resource. Finally, the field of KA can be transformed from its current state by a shift in the technologies available for it. There are two obvious directions of research to exploit here. First, there is the harnessing of the Internet. As noted above, there have been integrated sets of KA tools in workbenches, but such integration can be carried a step further by integrating the tools in a web-based environment, thereby allowing, e.g. the use of web-based libraries of knowledge model components, or the generation of knowledge models in XML.

The second direction of technological change for KA is that of software agents. Much KA, e.g. from information repositories on the WWW, might be automated by using intelligent agents, armed with structuring schemata such as ontologies of the domain in question.

AKT Technologies addressing issues in Knowledge Acquisition --

• AKT Research Map
  A competence map for members of the AKT project
  
  
• ANNIE - Open Source Information Extraction
  An open-source robust information extraction system
  
  
• Adaptiva
  A user-centred ontology building environment, based on using multiple strategies to construct an ontology, minimising user input by using adaptive information extraction.
  
  
• Amilcare
  An adaptive information extraction tool designed to support document annotation for the Semantic Web.
  
  
• Applications of FCA in AKT
  Formal Concept Analysis (FCA) is used in a variety of application scenarios in AKT in order to perform concept-based domain analysis and automatically deduce a taxonomy lattice of that domain.
  
  
• Armadillo
  Exploits the redundancies apparent in the Internet, combining many information sources to perform document annotation with minimal human intervention.
  
  
• Automatic Support for Enterprise Modelling and Workflow
  Knowledge management using multi-modelling techniques and how modelling activities may be assisted with automation based on formal methods.
  
  
• COCKATOO
  A knowledge acquisition tool which can be used to produce a set of cases for use with a Case-Based Reasoning system.
  
  
• COHSE - Conceptual Open Hypermedia Services Environment
  COHSE researches methods to improve significantly the quality, consistency and breadth of linking of WWW documents at retrieval and authoring time.
  
  
• ClassAKT
  A text classification web service for classifying documents according to the ACM Computing Classification System.
  
  
• Compendium
  Compendium is a semantic, visual hypertext tool for supporting collaborative domain modelling and real time meeting capture
  
  
• Dome
  A programmable XML editor which is being used in a knowledge extraction role to transform Web pages into RDF.
  
  
• Eprep
  An add-on for the Eprints document archive which uses text extraction to automatically create the bibliographic metadata needed for the submission of a new document.
  
  
• Foxtrot
  Foxtrot is a recommender system which represents user profiles in ontological terms, allowing inference, bootstrapping and profile visualization.
  
  
• GATE - General Architecture for Text Engineering
  GATE is a stable, robust, and scalable open-source infrastructure which allows users to build and customise language processing components, while it handles mundane tasks like data storage, format analysis and data visualisation.
  
  
• I-X Process Panels
  The I-X tool suite supports principled collaborations of human and computer agents in the creation or modification of some product.
  
  
• ILP for Information Extraction
  To overcome the knowledge acquisition bottleneck, we apply Inductive Logic Programming techniques to learn Information Extraction rules.
  
  
• Internet Reasoning Service
  The Internet Reasoning Service provides a a number of tools which supports the publication, location, composition and execution of heterogeneous web services, specified using semantic web technology
  
  
• KRAFT - I-X TIE
  Supports collaboration among members of a virtual organisation by integrating workflow and communication technology with constraint solving.
  
  
• Melita
  Melita is a semi-automatic annotation tool using an Adaptive Information Extraction engine (Amilcare)to support the user in document annotation.
  
  
• OntoPortal
  Enables the authoring and navigation of large semantically-powered portals
  
  
• ReTAX+
  ReTAX is an aide to help a taxonomist create a consistent taxonomy and in particular provides suggestions as to where a new entity could be placed in the taxonomy whilst retaining the integrity of the revised taxonomy (c.f., problems in ontology modelling).
  
  
• Refiner++
  REFINER++ is a system which allows domain experts to create and maintain their own Knowledge Bases, and to receive suggestions as to how to remove inconsistencies, if they exist.
  
  
• Semantic Annotation with MnM
  MnM is a semantic annotation tool which provides manual, automated and semi-automated support for annotating web pages with 'semantics', i.e., machine interpretable descriptions.
  
  
• eServices
  The e-Services framework provides advanced scholarly services (in particular visualisations) using distributed metadata.