Construction and Completion of Operational Entity Relationship Knowledge Graph

Jia, Zhen; Cheng, Guangquan; Huang, Kuihua; Chu, Yang

doi:10.1109/bigdia56350.2022.9874141

Cited by 2 publications

(1 citation statement)

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“…The Tonghuashun 2 company constructed an enterprise knowledge graph to show the upstream and downstream supply and demand relationships of enterprises in the industry supply chain. The China Merchants Bank introduced a knowledge graph of enterprise association relationships to display a variety of inter-enterprise relationships (Jia, 2022 ). Numerous studies and applications have shown that knowledge graphs can form an effective structured representation of knowledge, have rich semantic information representation capabilities and flexible graph structures, and are effective carriers of knowledge and information.…”

Section: Introductionmentioning

confidence: 99%

A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph

Yan,

Fang,

Huang

et al. 2023

Front. Big Data

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The knowledge graph is one of the essential infrastructures of artificial intelligence. It is a challenge for knowledge engineering to construct a high-quality domain knowledge graph for multi-source heterogeneous data. We propose a complete process framework for constructing a knowledge graph that combines structured data and unstructured data, which includes data processing, information extraction, knowledge fusion, data storage, and update strategies, aiming to improve the quality of the knowledge graph and extend its life cycle. Specifically, we take the construction process of an enterprise knowledge graph as an example and integrate enterprise register information, litigation-related information, and enterprise announcement information to enrich the enterprise knowledge graph. For the unstructured text, we improve existing model to extract triples and the F1-score of our model reached 72.77%. The number of nodes and edges in our constructed enterprise knowledge graph reaches 1,430,000 and 3,170,000, respectively. Furthermore, for each type of multi-source heterogeneous data, we apply corresponding methods and strategies for information extraction and data storage and carry out a detailed comparative analysis of graph databases. From the perspective of practical use, the informative enterprise knowledge graph and its timely update can serve many actual business needs. Our proposed enterprise knowledge graph has been deployed in HuaRong RongTong (Beijing) Technology Co., Ltd. and is used by the staff as a powerful tool for corporate due diligence. The key features are reported and analyzed in the case study. Overall, this paper provides an easy-to-follow solution and practice for domain knowledge graph construction, as well as demonstrating its application in corporate due diligence.

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Section: Introductionmentioning

confidence: 99%