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Context-based term identification and extraction for ontology construction
Abstract Ontology construction often requires a domOntology construction often requires a domain specific corpus in conceptualizing the domain knowledge; specifically, it is an association of terms, relation between terms and related instances. It is a vital task to identify a list of significant term for constructing a practical ontology. In this paper, we present the use of a context-based term identification and extraction methodology for ontology construction from text document. The methodology is using a taxonomy and Wikipedia to support automatic term identification and extraction from structured documents with an assumption of candidate terms for a topic are often associated with its topic-specific keywords. A hierarchical relationship of super-topics and sub-topics is defined by a taxonomy, meanwhile, Wikipedia is used to provide context and background knowledge for topics that defined in the taxonomy to guide the term identification and extraction. The experimental results have shown the context-based term identification and extraction methodology is viable in defining topic concepts and its sub-concepts for constructing ontology. The experimental results have also proven its viability to be applied in a small corpus / text size environment in supporting ontology construction.nment in supporting ontology construction.
Abstractsub Ontology construction often requires a domOntology construction often requires a domain specific corpus in conceptualizing the domain knowledge; specifically, it is an association of terms, relation between terms and related instances. It is a vital task to identify a list of significant term for constructing a practical ontology. In this paper, we present the use of a context-based term identification and extraction methodology for ontology construction from text document. The methodology is using a taxonomy and Wikipedia to support automatic term identification and extraction from structured documents with an assumption of candidate terms for a topic are often associated with its topic-specific keywords. A hierarchical relationship of super-topics and sub-topics is defined by a taxonomy, meanwhile, Wikipedia is used to provide context and background knowledge for topics that defined in the taxonomy to guide the term identification and extraction. The experimental results have shown the context-based term identification and extraction methodology is viable in defining topic concepts and its sub-concepts for constructing ontology. The experimental results have also proven its viability to be applied in a small corpus / text size environment in supporting ontology construction.nment in supporting ontology construction.
Bibtextype inproceedings  +
Doi 10.1109/NLPKE.2010.5587801  +
Has author Goh H.-N. + , Kiu C.-C. +
Has extra keyword Background knowledge + , Context-based + , Domain knowledge + , Domain specific + , Ontology construction + , Structured document + , Term identification and extraction + , Text document + , Wikipedia + , Computational linguistics + , Knowledge engineering + , Natural language processing systems + , Taxonomies + , Ontology +
Has keyword Ontology construction + , Taxonomy + , Term identification and extraction + , Wikipedia +
Isbn 9781424468966  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Published in Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2010 +
Title Context-based term identification and extraction for ontology construction +
Type conference paper  +
Year 2010 +
Creation dateThis property is a special property in this wiki. 7 November 2014 16:55:03  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 16:55:03  +
DateThis property is a special property in this wiki. 2010  +
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