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
Copyright Status
Unknown
Socpus ID
78651284486 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/78651284486
STARS Citation
Szumlanski, Sean and Gomez, Fernando, "Automatically Acquiring A Semantic Network Of Related Concepts" (2010). Scopus Export 2010-2014. 436.
https://stars.library.ucf.edu/scopus2010/436