Document (#40812)

Author
Ma, N.
Zheng, H.T.
Xiao, X.
Title
¬An ontology-based latent semantic indexing approach using long short-term memory networks
Source
Web and Big Data: First International Joint Conference, APWeb-WAIM 2017, Beijing, China, July 7-9, 2017, Proceedings, Part I. Eds.: L. Chen et al
Imprint
Cham : Springer
Year
2017
Pages
S.185-199
Series
Lecture notes in computer science; vol.10366
Abstract
Nowadays, online data shows an astonishing increase and the issue of semantic indexing remains an open question. Ontologies and knowledge bases have been widely used to optimize performance. However, researchers are placing increased emphasis on internal relations of ontologies but neglect latent semantic relations between ontologies and documents. They generally annotate instances mentioned in documents, which are related to concepts in ontologies. In this paper, we propose an Ontology-based Latent Semantic Indexing approach utilizing Long Short-Term Memory networks (LSTM-OLSI). We utilize an importance-aware topic model to extract document-level semantic features and leverage ontologies to extract word-level contextual features. Then we encode the above two levels of features and match their embedding vectors utilizing LSTM networks. Finally, the experimental results reveal that LSTM-OLSI outperforms existing techniques and demonstrates deep comprehension of instances and articles.
Content
Vgl.: https://link.springer.com/chapter/10.1007/978-3-319-63579-8_15. DOI: https://doi.org/10.1007/978-3-319-63579-8_15.
Theme
Wissensrepräsentation
Semantisches Umfeld in Indexierung u. Retrieval
Automatisches Indexieren
Object
Latent Semantic Indexing

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