Populating CRAB ontology using context-profile based approaches

Authors:
Lian Shi;Jigui Sun;Haiyan Che
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, China and Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education, ChangChun, China;College of Computer Science and Technology, Jilin University, Changchun, China and Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education, ChangChun, China;College of Computer Science and Technology, Jilin University, Changchun, China and Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education, ChangChun, China
Venue:
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Year:
2007

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Abstract

Ontologies are widely used for capturing and organizing knowledge of a particular domain of interest, and they play a key role in the Semantic Web version, which adds a machine tractable, repurposeable layer to complement the existing web of natural language hypertext. Semantic annotation of information with respect to a underlying ontology makes it machine-processable and allows for exchanging these information between various communities. This paper investigated approaches for Ontology Population from the Web or some big corpus and proposed context-profile based approaches for Ontology Population. For each term extracted from web sites and web documents, we build a context profile of the term. The context profiles are represented as vectors such that we can calculate the similarity of two vectors. In our experiments we populate the CRAB Ontology with new instances extracted by presented approaches. Both theory and experimental results have shown that our methods are inspiring and efficient.