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Descripción
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| Traditional distributional semantic models (DSMs) like Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) derive representations for words assuming words occurring in similar contexts will have similar representations. Deep Learning has made feasible the derivation of word embeddings (i.e. distributed word representations) from corpora of billions of words applying neural language models like CBOW and Skip-gram. The application of Deep Learning to aid ontology development remains largely unexplored. This study investigates the performance of LSA, LDA, CBOW and Skip-gram for ontology learning tasks. We conducted six experiments; firstly using 300K and later with 14M PubMed titles and abstracts to obtain topranked candidate terms related to the patient safety domain. Based on the evaluation performed, we conclude that Deep Learning can contribute to ontology engineering from the biomedical literature. Keywords. Ontology Learning, Deep Learning, OWL-DL, CBOW, Skip-gram | |
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Internacional
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Si |
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Nombre congreso
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9th International Conference SWAT4LS. Semantic Web Applications and Tools for the Life Sciences |
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Tipo de participación
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960 |
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Lugar del congreso
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Amsterdam |
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Revisores
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Si |
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ISBN o ISSN
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1613-0073 |
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DOI
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Fecha inicio congreso
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05/12/2016 |
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Fecha fin congreso
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08/12/2016 |
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Desde la página
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42 |
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Hasta la página
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52 |
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Título de las actas
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Semantic Web Applications and Tools for Life Sciences Proceedings of the 9th International Conference Semantic Web Applications and Tools for Life Sciences Amsterdam, The Netherlands, December 5-8, 2016. |