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International Conference on Database Systems for Advanced Applications

DASFAA 2019: Database Systems for Advanced Applications pp 3–18Cite as

A Probabilistic Approach for Inferring Latent Entity Associations in Textual Web Contents

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11448))

Abstract

Latent entity associations (EA) represent that two entities associate with each other indirectly through multiple intermediate entities in different textual Web contents (TWCs) including e-mails, Web news, social network pages, etc. In this paper, by adopting Bayesian Network as the framework to represent and infer latent EAs as well as the probabilities of associations, we propose the concept of entity association Bayesian Network (EABN). To construct EABN efficiently, we employ self-organizing map for TWC dataset division to make the co-occurrence-based dependence of each pair of entities concern just a small set of documents. Using probabilistic inferences of EABN, we evaluate and rank EAs in all possible entity pairs, by which novel latent EAs could be found. Experimental results show the effectiveness and efficiency of our approach.

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Acknowledgement

This paper was supported by the National Natural Science Foundation of China (U1802271), Program for the second Batch of Yunling Scholar of Yunnan Province (C6153001), Donglu Scholar Cultivation Project of Yunnan University, and Research Foundation of Educational Department of Yunnan Province (2016ZZX006).

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Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, China

    Lei Li, Kun Yue & Binbin Zhang

  2. School of Resource Environment and Earth Science, Yunnan University, Kunming, China

    Zhengbao Sun

Authors
  1. Lei Li
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  2. Kun Yue
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  3. Binbin Zhang
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  4. Zhengbao Sun
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Corresponding author

Correspondence to Kun Yue .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Guoliang Li

  2. Duke University, Durham, NC, USA

    Jun Yang

  3. University of Porto, Porto, Portugal

    Joao Gama

  4. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

  5. Beihang University, Beijing, China

    Yongxin Tong

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Li, L., Yue, K., Zhang, B., Sun, Z. (2019). A Probabilistic Approach for Inferring Latent Entity Associations in Textual Web Contents. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-18590-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18589-3

  • Online ISBN: 978-3-030-18590-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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