Computer Science ›› 2020, Vol. 47 ›› Issue (11): 237-243.doi: 10.11896/jsjkx.191000015

• Artificial Intelligence • Previous Articles     Next Articles

Construction and Application of Enterprise Risk Knowledge Graph

CHEN Xiao-jun, XIANG Yang   

  1. College of Electronic and Information Engineering,Tongji University,Shanghai 201804,China
  • Received:2019-10-08 Revised:2020-03-20 Online:2020-11-15 Published:2020-11-05
  • About author:CHEN Xiao-jun,born in 1995,Ph.D,is a student member of China Computer Federation.His main research interests include knowledge graph reasoning and knowledge representation learning.
    XIANG Yang,born in 1962,professor,Ph.D supervisor,is a senior member of China Computer Federation.His main research interests include artificial intelligence and natural language processing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (71571136).

Abstract: In supporting semantic Web,knowledge graphs have played an important role in many areas such as search engine,intelligent question-answering system,and recommender system.Therefore,they have become a hot topic in the field of artificial intelligence.Knowledge graphs have many advantages in graph display,mining,and computing,which can help enterprises or financial practitioners analyze and make decisions on business scenarios.At present,some companies have applied knowledge graphs in the financial domain,but these knowledge graphs still suffer from incompleteness.And most existing methods only focus on certain aspects when building financial knowledge graphs.Aiming at these problems above,this paper engages a systematic study on the domain knowledge graph and construct an enterprise risk knowledge graph.This paper describes the construction process of domain knowledge graph from the aspects of ontology construction,knowledge extraction,knowledge fusion,and knowledge storage.Based on the enterprise risk knowledge graph,an intelligent question-answering chatbot is developed to realize the retrieval and application of KG.In order to improve the accuracy of the question answering system,a character-based BiLSTM-CRF model for named entity recognition is used.Experimental results show that the character-based BiLSTM-CRF model performs better than the baseline when the sample size is small.

Key words: Enterprise risk, Knowledge extraction, Knowledge fusion, Knowledge graph, Ontology, Question-answering system

CLC Number: 

  • TP391
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