Title :
An Ontology Term Extracting Method Based on Latent Dirichlet Allocation
Author :
Yu Jing ; Wang Junli ; Zhao Xiaodong
Author_Institution :
Res. Center of CAD, Tongji Univ., Shanghai, China
Abstract :
Ontology plays an important part on Semantic Web, Information Retrieval, and Intelligent Information Integration etc. Ontology learning gets widely studied due to many problems in totally manual ontology construction. Term extraction influences many respects of ontology learning as it´s the basis of ontology learning hierarchical structure. This paper mines topics of the corpus based on Latent Dirichlet Allocation (LDA) which uses Variational Inference and Expectation-Maximization (EM) Algorithm to estimate model parameters. With the help of irrelevant vocabulary, the paper provides better experimental results which show that the distribution of topics on terms reveals latent semantic features of the corpus and relevance among words.
Keywords :
data mining; expectation-maximisation algorithm; inference mechanisms; information retrieval; learning (artificial intelligence); ontologies (artificial intelligence); semantic Web; EM algorithm; LDA; expectation-maximization algorithm; information retrieval; intelligent information integration; latent Dirichlet allocation; model parameter estimation; ontology construction; ontology learning hierarchical structure; ontology term extracting method; semantic Web; term extraction; variational inference algorithm; Data mining; Educational institutions; Inference algorithms; Ontologies; Probabilistic logic; Semantics; Vocabulary; LDA; topic mining; ontology learning; Variational Inference; EM algorithm; LDA;
Conference_Titel :
Multimedia Information Networking and Security (MINES), 2012 Fourth International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-3093-0
DOI :
10.1109/MINES.2012.71
