Automatically acquiring a semantic network of related concepts
|Automatically acquiring a semantic network of related concepts|
|Author(s)||Szumlanski S., Gomez F.|
|Published in||International Conference on Information and Knowledge Management, Proceedings|
|Keyword(s)||Common sense knowledge, Knowledge acquisition, Lexical semantics, Semantic networks, Semantic relatedness (Extra: Automatic construction, Co-occurrence, Commonsense knowledge, Lexical semantics, Semantic network, Semantic networks, Semantic relatedness, Wikipedia, Wordnet, Information theory, Knowledge acquisition, Knowledge management, Ontology, Semantic Web, Semantics)|
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Automatically acquiring a semantic network of related concepts is a 2010 conference paper written in English by Szumlanski S., Gomez F. and published in International Conference on Information and Knowledge Management, Proceedings.
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.
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