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| Research on Financial Knowledge Representation and Risk Identification from Knowledge Connection Perspective |
| Tang Xuli, Ma Feicheng, Fu Weigang, Zhang Rui |
| Center for the Studies of Information Resources, Wuhan University, Wuhan 430072 |
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Abstract The limitations of financial risk analysis, such as homogeneous data source, simplistic data types, and one-sided research perspective, are attributed to insufficient representation of big data in finance; the traditional flat data organization ignores the rich knowledge connection among financial data. This paper explores the knowledge representation model of big data on financials from a knowledge organization perspective. It classifies the process of knowledge representation into three layers: knowledge representation mode, knowledge instance, and knowledge mining. First, the paper summarizes the typical connection patterns that exist in the financial domain, such as classification connection, spatial-temporal connection, statistical connection, and event connection, and categorizes these into static ontology, dynamic ontology, and social ontology according to the differential realization. Then, following the implementation of each ontology, it proposes related implementation schemes as well as the reuse of the existing ontology of Financial Industry Business Ontology (FIBO). Lastly, it demonstrates the representation process using the case of financial risk identification.
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Received: 23 July 2018
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