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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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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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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