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Multi-dimension topic mining based on hierarchical semantic graph model

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dc.contributor.authorZhang, T.
dc.contributor.authorLee, B.
dc.contributor.authorZhu, Q.
dc.contributor.authorHan, X.
dc.contributor.authorYe, E.M.
dc.date.issued2020
dc.description.abstractTopic mining of scientific literature can accurately capture the contextual structure of a topic, track research hotspots within a field, and improve the availability of information about the literature. This paper introduces a multi-dimensional topic mining method based on a hierarchical semantic graph model. The main innovations include (1) the hierarchical extraction of feature terms and construction of a corresponding semantic graph and (2) multi-dimensional topic mining based on graph segmentation and structure analysis. The process of semantic graph construction is based primarily on hierarchical feature term extraction, which can effectively reveal the hierarchical structural distribution of feature terms within documents. Our graph model also takes into account the complementarity of content- and context-related feature terms in documents while avoiding the loss of textual information. In addition, the multi-dimensional features of the topic can be mined effectively via an in-depth analysis of the constructed graph, resulting in a quantitative visualization of the many-to-many association between the topic and feature terms. A variety of experiments on existing document datasets demonstrate that the proposed approach is able to outperform state-of-the-art methods in terms of accuracy and efficacy.
dc.identifier.citationIEEE Access, 2020; 8(9050803):64820-64835
dc.identifier.doi10.1109/ACCESS.2020.2984352
dc.identifier.issn2169-3536
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/11541.2/142344
dc.language.isoen
dc.publisherIEEE
dc.rightsCopyright 2020 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information (https://creativecommons.org/licenses/by/4.0/)
dc.source.urihttps://doi.org/10.1109/ACCESS.2020.2984352
dc.subjecthierarchical semantic graph
dc.subjectmulti-dimensional topic
dc.subjecttopic mining
dc.titleMulti-dimension topic mining based on hierarchical semantic graph model
dc.typeJournal article
pubs.publication-statusPublished
ror.fileinfo12197269110001831 13197278870001831 09050803.pdf
ror.mmsid9916398606101831

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