Improving Semantic Relatedness Assessments: Ontologies Meet Textual Corpora
| Autor: | Montserrat Batet, David Sánchez |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
semantic similarity
knowledge Computer science InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL 02 engineering and technology Ontology (information science) Semantics computer.software_genre Semantic similarity Semantic equivalence 020204 information systems Semantic computing 0202 electrical engineering electronic engineering information engineering Semantic analytics ontologies Semantic integration semantics Semantic compression General Environmental Science Information retrieval business.industry Semantic search Ontology General Earth and Planetary Sciences 020201 artificial intelligence & image processing Artificial intelligence semantic relatednes business textual information distribution computer Natural language processing |
| Zdroj: | KES |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2016.08.149 |
| Popis: | Even though the calculation of the semantic similarity between textual entities has received a lot of attention by the research community, the more general notion of semantic relatedness (which considers both taxonomic and non-taxonomic knowledge) has been significantly less studied and, in general, stays one step behind in terms of accuracy. In this paper, we improve semantic relatedness assessments by aggregating the highly-accurate ontology-based estimation of semantic similarity with the distributional resemblance of textual terms computed from large textual corpora. As a result, our approach is able to improve the accuracy of related works on a standard benchmark. |
| Databáze: | OpenAIRE |
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