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

Chenwei Yan1,2, Xinyue Fang3, Xiaotong Huang1,2

  • 1School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China.

Frontiers in Big Data
|October 16, 2023
PubMed
Summary
This summary is machine-generated.

We developed a framework for building enterprise knowledge graphs from diverse data sources, enhancing artificial intelligence infrastructure. This approach improves data quality and extends the knowledge graph

Keywords:
enterprise knowledge graphgraph databaseheterogeneous dataknowledge graph constructionknowledge graph update

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Area of Science:

  • Artificial Intelligence
  • Knowledge Engineering
  • Data Science

Background:

  • Constructing high-quality domain knowledge graphs from multi-source heterogeneous data presents significant challenges.
  • Knowledge graphs are essential infrastructure for artificial intelligence applications.
  • Existing methods often struggle with integrating diverse data types effectively.

Purpose of the Study:

  • To propose a comprehensive framework for constructing domain knowledge graphs by integrating structured and unstructured data.
  • To enhance the quality and extend the life cycle of knowledge graphs.
  • To demonstrate the practical application of an enterprise knowledge graph in corporate due diligence.

Main Methods:

  • Developed a complete process framework including data processing, information extraction, knowledge fusion, data storage, and update strategies.
  • Integrated enterprise register, litigation, and announcement information for an enterprise knowledge graph.
  • Improved existing models for extracting triples from unstructured text, achieving an F1-score of 72.77%.

Main Results:

  • Constructed an enterprise knowledge graph with 1,430,000 nodes and 3,170,000 edges.
  • Applied specific methods for information extraction and data storage for multi-source heterogeneous data.
  • Conducted a comparative analysis of graph databases.

Conclusions:

  • The proposed framework offers a practical solution for domain knowledge graph construction.
  • The developed enterprise knowledge graph is deployed and utilized for corporate due diligence.
  • Timely updates and informative knowledge graphs serve critical business needs.