Abstract
Domain knowledge graphs, which compose scattered information about domain entities, are expressive when organizing information for enterprise systems in the decision-making process. Such knowledge graphs can give us semantically-rich information which can later be applied to fuel different graph mining services to conduct analytical work. In this paper, we discuss a subject-oriented domain knowledge graph based on multi-source heterogenous data consisting of dynamic data generated from daily transactions among companies in interlacing supply-chains and relatively static data demonstrating the basic properties of these enterprises to assist with analytical work. Such high-dimensional graph with strong heterogeneity is rich in semantics and is casted into lower dimensions to be used as inputs for graph mining services, giving us various enterprise correlation chains, aiming to support upper-level application like credit risk assessment. The framework has been testified in real-life information systems.
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Acknowledgment
This research is supported by the Development of E-commerce Service Platform Architecture and Data Service Project under Grant 2017C02036.
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Zhang, W., Liu, Y., Jiang, L., Shah, N., Fei, X., Cai, H. (2020). The Construction of a Domain Knowledge Graph and Its Application in Supply Chain Risk Analysis. In: Chao, KM., Jiang, L., Hussain, O., Ma, SP., Fei, X. (eds) Advances in E-Business Engineering for Ubiquitous Computing. ICEBE 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-030-34986-8_33
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DOI: https://doi.org/10.1007/978-3-030-34986-8_33
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