Advertisement

The Construction of a Domain Knowledge Graph and Its Application in Supply Chain Risk Analysis

  • Wanyue Zhang
  • Yonggang Liu
  • Lihong Jiang
  • Nazaraf Shah
  • Xiang Fei
  • Hongming CaiEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 41)

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 risk 

Notes

Acknowledgment

This research is supported by the Development of E-commerce Service Platform Architecture and Data Service Project under Grant 2017C02036.

References

  1. 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. 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. 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. 4.
    Heflin, J., Song, D.: Ontology instance linking: towards interlinked knowledge graphs, p. 7 (2016)Google Scholar
  5. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 17.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Wanyue Zhang
    • 1
  • Yonggang Liu
    • 2
  • Lihong Jiang
    • 1
  • Nazaraf Shah
    • 3
  • Xiang Fei
    • 3
  • Hongming Cai
    • 1
    Email author
  1. 1.Shanghai Jiao Tong UniversityShanghaiChina
  2. 2.Nanjing Runchain Technology Co. Ltd.NanjingChina
  3. 3.Coventry UniversityConventryUK

Personalised recommendations