A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Big Data |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2023.1278153/full |
| _version_ | 1827804214916546560 |
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| author | Chenwei Yan Chenwei Yan Xinyue Fang Xiaotong Huang Xiaotong Huang Chenyi Guo Ji Wu |
| author_facet | Chenwei Yan Chenwei Yan Xinyue Fang Xiaotong Huang Xiaotong Huang Chenyi Guo Ji Wu |
| author_sort | Chenwei Yan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T21:07:42Z |
| format | Article |
| id | doaj.art-4d40564507fe40959c9e7e78e7d6bbcb |
| institution | Directory Open Access Journal |
| issn | 2624-909X |
| language | English |
| last_indexed | 2024-03-11T21:07:42Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj.art-4d40564507fe40959c9e7e78e7d6bbcb2023-09-29T09:17:29ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2023-09-01610.3389/fdata.2023.12781531278153A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graphChenwei Yan0Chenwei Yan1Xinyue Fang2Xiaotong Huang3Xiaotong Huang4Chenyi Guo5Ji Wu6School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, ChinaKey Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Economics and Management, Tsinghua University, Beijing, ChinaSchool of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, ChinaKey Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing, ChinaThe 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.https://www.frontiersin.org/articles/10.3389/fdata.2023.1278153/fullknowledge graph constructionheterogeneous dataknowledge graph updateenterprise knowledge graphgraph database |
| spellingShingle | Chenwei Yan Chenwei Yan Xinyue Fang Xiaotong Huang Xiaotong Huang Chenyi Guo Ji Wu A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph Frontiers in Big Data knowledge graph construction heterogeneous data knowledge graph update enterprise knowledge graph graph database |
| title | A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph |
| title_full | A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph |
| title_fullStr | A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph |
| title_full_unstemmed | A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph |
| title_short | A solution and practice for combining multi-source heterogeneous data to construct enterprise knowledge graph |
| title_sort | solution and practice for combining multi source heterogeneous data to construct enterprise knowledge graph |
| topic | knowledge graph construction heterogeneous data knowledge graph update enterprise knowledge graph graph database |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2023.1278153/full |
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