Abstract
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).
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Acknowledgements
We thank the anonymous reviewer for their helpful comments. We acknowledge support from the EPSRC funded project named A Situation Aware Information Infrastructure Project (EP/L026015) and the Integrated Multimedia City Data (IMCD), a project within the ESRC-funded Urban Big Data Centre (ES/L011921/1). This work was also partly supported by NSF grant #61572223. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the sponsor.
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Chen, L., Jose, J.M., Yu, H., Yuan, F., Zhang, H. (2016). Probabilistic Topic Modelling with Semantic Graph. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_18
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DOI: https://doi.org/10.1007/978-3-319-30671-1_18
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