The Construction of a Domain Knowledge Graph and Its Application in Supply Chain Risk Analysis
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.
Keywords
Domain ontology Knowledge graph construction Community detection Data as a service Supply chain riskNotes
Acknowledgment
This research is supported by the Development of E-commerce Service Platform Architecture and Data Service Project under Grant 2017C02036.
References
- 1.Xu, B., Xie, C., Cai, H.: Application of domain-ontology method in meta-data management of data warehouse. Appl. Res. Comput. 11(27), 4162–4164 (2010)Google Scholar
- 2.Sun, H., Ren, R., Cai, H., et al.: Topic model based knowledge graph for entity similarity measuring. In: 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE), IEEE, pp. 94–101 (2018)Google Scholar
- 3.Gomez-Perez, J.M., Pan, J.Z., Vetere, G., Wu, H.: Enterprise knowledge graph: an introduction. In: Pan, J.Z., Vetere, G., Gomez-Perez, J.M., Wu, H. (eds.) Exploiting Linked Data and Knowledge Graphs in Large Organisations, pp. 1–14. Springer, Cham (2017)Google Scholar
- 4.Heflin, J., Song, D.: Ontology instance linking: towards interlinked knowledge graphs, p. 7 (2016)Google Scholar
- 5.Ruan, T., Xue, L., Wang, H., Hu, F., Zhao, L., Ding, J.: Building and exploring an enterprise knowledge graph for investment analysis. In: Groth, P., Simperl, E., Gray, A., Sabou, M., Krötzsch, M., Lecue, F., Flöck, F., Gil, Y. (eds.) The Semantic Web – ISWC 2016, vol. 9982, pp. 418–436. Springer, Cham (2016)CrossRefGoogle Scholar
- 6.Moody, D.L., Kortink, M.A.R.: From enterprise models to dimensional models: a methodology for data warehouse and data mart design. In: DMDW, p. 5 (2000)Google Scholar
- 7.Bakalash, R., Shaked, G., Caspi, J.: Enterprise-wide data-warehouse with integrated data aggregation engine: U.S. Patent 7,315,849, 1 January 2008Google Scholar
- 8.Cai, H., et al.: IoT-based configurable information service platform for product lifecycle management. IEEE Trans. Ind. Inform. 10(2), 1558–1567 (2014)CrossRefGoogle Scholar
- 9.Tang, L., Liu, H.: Community Detection and Mining in Social Media, Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan and Claypool, California (2010)Google Scholar
- 10.Bedi, P., Sharma, C.: Community detection in social networks. Wiley Interdisc. Rev. Data Min. Knowl. Discovery 6(3), 115–135 (2016)CrossRefGoogle Scholar
- 11.Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University (2002)Google Scholar
- 12.Xie, J., Szymanski, B.K., Liu, X.: Slpa: Uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: 2011 IEEE 11th International Conference on Data Mining Workshops, IEEE, pp. 344–349 (2011)Google Scholar
- 13.Orman, G.K., Labatut, V., Cherifi, H.: Comparative evaluation of community detection algorithms: a topological approach. J. Stat. Mech. Theory Exp. 2012(08), P08001 (2012)CrossRefGoogle Scholar
- 14.Wu, Z.H., Lin, Y.F., Gregory, S., et al.: Balanced multi-label propagation for overlapping community detection in social networks. J. Comput. Sci. Technol. 27(3), 468–479 (2012)MathSciNetCrossRefGoogle Scholar
- 15.Yu, H., Cai, H., Zhou, J., et al.: Data service generation framework from heterogeneous printed forms using semantic link discovery. Future Gener. Comput. Syst. 79, 514–527 (2018)CrossRefGoogle Scholar
- 16.Zhang, S., Miao, Q., et al.: A Management Method and Platform of Credit Exchange Based on Supply Chain Finance. CN:107767269 (2012)Google Scholar
- 17.The Service Ontology. https://dini-ag-kim.github.io/service-ontology/service.html