Graph-based domain-specific semantic relatedness from Wikipedia
|Graph-based domain-specific semantic relatedness from Wikipedia|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Biomedical Domain, Semantic Relatedness, Wikipedia Mining (Extra: Artificial intelligence, Data mining, Graphic methods, Semantics, Biomedical domain, Domain-specific application, Graph-based methods, Lexical resources, Ontology-based methods, Semantic relatedness, Semantic relationships, Wikipedia minings, Natural language processing systems)|
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Graph-based domain-specific semantic relatedness from Wikipedia is a 2014 conference paper written in English by Sajadi A. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Human made ontologies and lexicons are promising resources for many text mining tasks in domain specific applications, but they do not exist for most domains. We study the suitability of Wikipedia as an alternative resource for ontologies regarding the Semantic Relatedness problem. We focus on the biomedical domain because (1) high quality manually curated ontologies are available and (2) successful graph based methods have been proposed for semantic relatedness in this domain. Because Wikipedia is not hierarchical and links do not convey defined semantic relationships, the same methods used on lexical resources (such as WordNet) cannot be applied here straightforwardly. Our contributions are (1) Demonstrating that Wikipedia based methods outperform state of the art ontology based methods on most of the existing ontologies in the biomedical domain (2) Adapting and evaluating the effectiveness of a group of bibliometric methods of various degrees of sophistication on Wikipedia for the first time (3) Proposing a new graph-based method that is outperforming existing methods by considering some specific features of Wikipedia structure.
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