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Query Associations Over Big Financial Knowledge Graph

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Big Scientific Data Management (BigSDM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11473))

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Abstract

Knowledge graph, as the core technology of artificial intelligence, is playing a more and more important role in the financial field. In this paper, we study the problem of querying associations over big financial knowledge graph formed by equity network. This type of queries is building block for many financial services, which reveal the complex equity structure that covers several vertices of interest including enterprises, shareholders and so on. Specifically, we propose efficient algorithms to find Top-k path with largest control power between two vertices, namely Dual-node Association Query (DAQ). Differently from typical path queries, first, there are heterogeneous edges in financial knowledge graph, including shareholding and holding. Second, DAQ calculates path weights based on product rather than sum of edge weights on the path. Further, we propose an efficient algorithm for Multi-node Association Query (MAQ) that generalizes DAQ. Experimental evaluation and extensive case study on a real financial knowledge graph demonstrate the efficiency and effectiveness of the proposed algorithms.

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Acknowledgments

The research is supported in part by National Science Foundation, China No. 91646206, and National Science Foundation, Hubei Province, No. 682, 2018.

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Authors and Affiliations

  1. School of Information Management, Wuhan University, Wuhan, 430072, China

    Liang Hong & Lujia Zhang

Authors
  1. Xiaofeng Ouyang
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  2. Liang Hong
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  3. Lujia Zhang
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Corresponding author

Correspondence to Liang Hong .

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Editors and Affiliations

  1. CNIC, CAS, Beijing, China

    Jianhui Li

  2. Renmin University of China, Beijing, China

    Xiaofeng Meng

  3. Centrum Wiskunde and Informatica, Amsterdam, The Netherlands

    Ying Zhang

  4. CNIC, CAS, Beijing, China

    Wenjuan Cui

  5. Tsinghua University, Beijing, China

    Zhihui Du

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Ouyang, X., Hong, L., Zhang, L. (2019). Query Associations Over Big Financial Knowledge Graph. In: Li, J., Meng, X., Zhang, Y., Cui, W., Du, Z. (eds) Big Scientific Data Management. BigSDM 2018. Lecture Notes in Computer Science(), vol 11473. Springer, Cham. https://doi.org/10.1007/978-3-030-28061-1_21

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  • DOI: https://doi.org/10.1007/978-3-030-28061-1_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28060-4

  • Online ISBN: 978-3-030-28061-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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