Extracting focused knowledge from the semantic web

✍ Scribed by LOUISE CROW; NIGEL SHADBOLT

• Publisher: Elsevier Science
• Year: 2001
• Tongue: English
• Weight: 1014 KB
• Volume: 54
• Category: Article
• ISSN: 1071-5819
• DOI: 10.1006/ijhc.2000.0453

✦ Synopsis

Ontologies are increasingly being recognized as a critical component in making networked knowledge accessible. Software architectures which can assemble knowledge from networked sources coherently according to the requirements of a particular task or perspective will be at a premium in the next generation of web services. We argue that the ability to generate task-relevant ontologies e$ciently and relate them to web resources will be essential for creating a machine-inferencable &&semantic web''. The Internet-based multi-agent problem solving (IMPS) architecture described here is designed to facilitate the retrieval, restructuring, integration and formalization of task-relevant ontological knowledge from the web. There are rich structured and semi-structured sources of knowledge available on the web that present implicit or explicit ontologies of domains. Knowledge-level models of tasks have an important role to play in extracting and structuring useful focused problem-solving knowledge from these web sources. IMPS uses a multi-agent architecture to combine these models with a selection of web knowledge extraction heuristics to provide clean syntactic integration of ontological knowledge from diverse sources and support a range of ontology merging operations at the semantic level. Whilst our speci"c aim is to enable on-line knowledge acquisition from web sources to support knowledge-based problem solving by a community of software agents encapsulating problem-sloving inferences, the techniques described here can be applied to more general task-based integration of knowledge from diverse web sources, and the provision of services such as the critical comparison, fusion, maintenance and update of both formal informal ontologies.

2001 Academic Press