Espinosa-Anke L., Oramas S, Camacho-Collados J, Sagion H. Finding and Expanding Hypernymic Relations in the Music Domain. 19th International Conference of the Catalan Association for Artificial Inteligence (CCIA 2016)
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Espinosa-Anke L., Oramas S, Camacho-Collados J, Sagion H. Finding and Expanding Hypernymic Relations in the Music Domain. 19th International Conference of the Catalan Association for Artificial Inteligence (CCIA 2016)
Espinosa-Anke L., Oramas S, Camacho-Collados J, Sagion H. Finding and Expanding Hypernymic Relations in the Music Domain. 19th International Conference of the Catalan Association for Artificial Inteligence (CCIA 2016)
Lexical taxonomies are tree or directed acyclic graph-like structures where each node represents a concept and each edge encodes a binary hypernymic (is-a) relation. These lexical resources are useful for AI tasks like Information Retrieval or Machine Translation. Two main trends exist in the construction and exploitation of these resources: On one hand, general purpose taxonomies like WordNet, and on the other,domain-specific databases such as the CheBi chemical ontology, or MusicBrainz in the music domain. In both cases these are based on finding correct hypernymic relations between pairs of concepts. In this paper, we propose a generic framework for hypernym discovery, based on exploiting linear relations between (term, hypernym) pairs in Wikidata, and apply it to the domain of music. Our promising results, based on several metrics used in Information Retrieval, show that in several cases we are able to discover the correct hypernym for a given novel term.
Keywords.Semantics, taxonomy learning, word sense disambiguation.