Title

Automatically Acquiring A Semantic Network Of Related Concepts

Keywords

Common sense knowledge; Knowledge acquisition; Lexical semantics; Semantic networks; Semantic relatedness

Abstract

We describe the automatic construction of a semantic network1, in which over 3000 of the most frequently occurring monosemous nouns2 in Wikipedia (each appearing between 1,500 and 100,000 times) are linked to their semantically related concepts in the WordNet noun ontology. Relatedness between nouns is discovered automatically from cooccurrence in Wikipedia texts using an information theoretic inspired measure. Our algorithm then capitalizes on salient sense clustering among related nouns to automatically dis-ambiguate them to their appropriate senses (i.e., concepts). Through the act of disambiguation, we begin to accumulate relatedness data for concepts denoted by polysemous nouns, as well. The resultant concept-to-concept associations, covering 17,543 nouns, and 27,312 distinct senses among them, constitute a large-scale semantic network of related concepts that can be conceived of as augmenting the WordNet noun ontology with related-to links. © 2010 ACM.

Publication Date

12-1-2010

Publication Title

International Conference on Information and Knowledge Management, Proceedings

Number of Pages

19-28

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/1871437.1871445

Socpus ID

78651284486 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/78651284486

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