Extracting Focused Knowledge from the Semantic Web
Extracting Focused Knowledge from the Semantic Web
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 efficiently 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 specific 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-solving 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.
155-84
Crow, L. R.
b41f0b75-8c1a-4272-b8b1-c0a3f8471a1c
Shadbolt, N. R.
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
2001
Crow, L. R.
b41f0b75-8c1a-4272-b8b1-c0a3f8471a1c
Shadbolt, N. R.
5c5acdf4-ad42-49b6-81fe-e9db58c2caf7
Crow, L. R. and Shadbolt, N. R.
(2001)
Extracting Focused Knowledge from the Semantic Web.
International Journal of Human-Computer Studies, 54 (1), .
Abstract
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 efficiently 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 specific 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-solving 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.
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Published date: 2001
Organisations:
Web & Internet Science
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Local EPrints ID: 254260
URI: http://eprints.soton.ac.uk/id/eprint/254260
PURE UUID: 38a1aeb1-a39b-45c3-8adf-14007d66e56a
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Date deposited: 06 Mar 2002
Last modified: 26 Apr 2022 21:33
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Author:
L. R. Crow
Author:
N. R. Shadbolt
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