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Enhancing Graph-Based Keywords Extraction with Node Association

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Knowledge Science, Engineering and Management (KSEM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11775))

Abstract

In this paper, we present an enhancing graph-based keywords extraction method with node association (GKENA), which strengths graph-based keywords extraction approaches by applying strong association rule mining and unifying three different node attributes into a single framework. Specifically, we regard one single document as a sequential transaction dataset, and apply an efficient algorithm to exploit closed frequent sets and the strong association rules are generated to represent the correlations among multiple terms for association graph construction. Each graph node represents combinations of two or more terms and three node attributes (i.e. graph structure, node semantics and associations) are unified to transfer extra node information into a Markov chain model to obtain the ranking. Besides, in order to avoid the semantic overlapping among top ranked candidate words, a trustworthy clustering algorithm is employed and the center word in each cluster is selected to construct the keywords sets. Our experiments on both Chinese and English datasets indicate that GKENA can boost the quality of keywords in graph-based keywords extraction techniques.

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Acknowledgments

The work is supported by the National Natural Science Foundation of China (No. 61762078, 61363058, 61802404, 61702508) Guangxi Key Laboratory of Trusted Software (No. kx201910) and Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (MIMS18-08), Key Research Program of Beijing Municipal Science & Technology Commission (Grant No. D18110100060000).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China

    Huifang Ma, Shuang Wang & Miao Li

  2. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Huifang Ma

  3. Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China

    Huifang Ma

  4. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Ning Li

Authors
  1. Huifang Ma
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  2. Shuang Wang
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  3. Miao Li
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  4. Ning Li
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Corresponding author

Correspondence to Huifang Ma .

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Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Christos Douligeris

  2. University of Vienna, Vienna, Austria

    Dimitris Karagiannis

  3. University of Piraeus, Piraeus, Greece

    Dimitris Apostolou

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Ma, H., Wang, S., Li, M., Li, N. (2019). Enhancing Graph-Based Keywords Extraction with Node Association. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_45

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  • DOI: https://doi.org/10.1007/978-3-030-29551-6_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29550-9

  • Online ISBN: 978-3-030-29551-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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