A semantic-grained perspective of latent knowledge modeling

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• 作者：Paola Della Rocca^a ; Sabrina Senatore ; ^a ; ^{ssenatore@unisa.it} ; Vincenzo Loia^b
• 关键词：Knowledge structuring ; Latent Dirichlet Allocation (LDA) ; Latent Semantic Analysis (LSA) ; Concept extraction ; Ontology ; SKOS
• 刊名：Information Fusion
• 出版年：2017
• 出版时间：July 2017
• 年：2017
• 卷：36
• 期：Complete
• 页码：52-67
• 全文大小：3666 K
• 卷排序：36

文摘

The paper introduces a semantically enhanced document retrieval system. Each retrieved document is semantically represented as a multi-grained concept net. The used latent models are Latent Semantic Analysis and Latent Dirichlet Allocation LSA and LDA feed the local knowledge modelled as ontology. The result is a SKOS ontology describing the whole knowledge graphically navigable.