2008 FromGlossariesToOntologies

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Subject Headings: Ontology Learning from Text, Semantic Relation Learning, Glossary Formalization.

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Author Keywords

Ontology Learning, Semantic Relation Learning, Glossary Formalization.

Abstract

Learning ontologies requires the acquisition of relevant domain concepts and taxonomic, as well as non-taxonomic, relations. In this chapter, we present a methodology for automatic ontology enrichment and document annotation with concepts and relations of an existing domain core ontology. Natural language definitions from available glossaries in a given domain are processed and regular expressions are applied to identify general-purpose and domain-specific relations. We evaluate the methodology performance in extracting hypernymy and nontaxonomic relations. To this end, we annotated and formalized a relevant fragment of the glossary of Art and Architecture (AAT) with a set of 10 relations (plus the hypernymy relation) defined in the CRM CIDOC cultural heritage core ontology, a recent W3C standard. Finally, we assessed the generality of the approach on a set of web pages from the domains of history and biography.

Introduction

The Semantic Web [1], i.e. the vision of a next-generation web where content is conceptually indexed, requires applications to process and exploit the semantics implicitly encoded in on-line and off-line resources. The large-scale, automatic semantic annotation of web documents based on well-established domain ontologies would allow Semantic Web applications to emerge and gain acceptance. Wide coverage ontologies are indeed available for general applications (e.g. WordNet2, CYC3, SUMO4), however semantic annotation in unconstrained areas seems still out of reach for state-of-the-art systems. Domain-specific ontologies are preferable since they would limit the semantic coverage needed and make the applications feasible.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2008 FromGlossariesToOntologiesRoberto Navigli
Paola Velardi		From Glossaries to Ontologies: Extracting Semantic Structure from Textual DefinitionsOntology Learning and Population: Bridging the Gap between Text and Knowledgehttp://www.dsi.uniroma1.it/~navigli/pubs/Navigli Velardi IOS 2008.pdf2008
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