Adaptiva fact-file

What's the Problem?

• Semantic Web depends on the creation of a large number of domain specific ontologies.
  
• Most actual or potential users of the Semantic Web are not experts in ontology construction.
• Existing ontology construction methodologies involve high level of expertise in the domain and the encoding process.
  
• Manual ontology is difficult, slow, time-consuming, tedious and costly.

Towards a Solution

Adaptiva is 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, minimising input by using adaptive information extraction.

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 ontology 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 exemples of the ontological relation under consideration.

In Adaptiva, we have integrated Amilcare, a tool for adaptive Information Extraction from text designed for supporting active annotation of documents for the Semantic Web. 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 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.

Further Reading

Key document:

Christopher Brewster, Fabio Ciravegna and Yorick Wilks:
"User-Centred Ontology Learning for Knowledge Management "
7th International Workshop on Applications of Natural Language to Information Systems, Stockholm, June 27-28, 2002, Lecture Notes in Computer Sciences, Springer Verlag.
Also in the eprints archive

Other relevant documents

Christopher Brewster, Fabio Ciravegna and Yorick Wilks:
"Knowledge Acquisition for Knowledge Management: Position Paper " in Proceeding of the   IJCAI-2001 Workshop on Ontology Learning   to be held in conjuction with the 17th International Conference on Artificial Intelligence (IJCAI-01), Seattle, August, 2001
Also in the eprints archive.

Christopher Brewster, "Techniques for Automated Taxonomy Building: Towards Ontologies for Knowledge Management ". In Proceedings CLUK Research Colloquium, Leeds, UK, 2002
Also in the eprints archive.

Semantic representation

View in the AKT Triplestore Browser or as RDF.

Also available in DOAP RDF (Description Of A Project)