Probabilistic Topic Modelling with Semantic Graph

Chen, L., Jose, J. M. , Yu, H., Yuan, F. and Zhang, H. (2016) Probabilistic Topic Modelling with Semantic Graph. Lecture Notes in Computer Science, 9626, pp. 240-251. (doi: 10.1007/978-3-319-30671-1_18)

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In this paper we propose a novel framework, topic model with semantic graph (TMSG), which couples topic model with the rich knowledge from DBpedia. To begin with, we extract the disambiguated entities from the document collection using a document entity linking system, i.e., DBpedia Spotlight, from which two types of entity graphs are created from DBpedia to capture local and global contextual knowledge, respectively. Given the semantic graph representation of the documents, we propagate the inherent topic-document distribution with the disambiguated entities of the semantic graphs. Experiments conducted on two real-world datasets show that TMSG can significantly outperform the state-of-the-art techniques, namely, author-topic Model (ATM) and topic model with biased propagation (TMBP).

Item Type:Articles
Glasgow Author(s) Enlighten ID:Jose, Professor Joemon and Chen, Dr Long and Yu, Dr Haitao
Authors: Chen, L., Jose, J. M., Yu, H., Yuan, F., and Zhang, H.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
643481A Situation-aware information infrastructureDimitrios PezarosEngineering & Physical Sciences Research Council (EPSRC)EP/L026015/1COM - COMPUTING SCIENCE
651921Urban Big Data Research CentrePiyushimita ThakuriahEconomic & Social Research Council (ESRC)ES/L011921/1SPS - URBAN STUDIES