Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6): 142-150.doi: 10.11896/jsjkx.230300071

• Database & Big Data & Data Science • Previous Articles     Next Articles

Intelligent Mapping Recommendation-based Knowledge Graph Instance Construction and Evolution Method

ZHANG Yaqing1,2, SHAN Zhongyuan1,2, ZHAO Junfeng1,2,3, WANG Yasha1,2,3   

  1. 1 School of Computer Science,Peking University,Beijing 100871,China
    2 Key Laboratory of High Confidence Software Technologies,Ministry of Education,Beijing 100871,China
    3 Peking University Information Technology Institute(Tianjin Binhai),Tianjin 300450,China
  • Received:2023-03-08 Revised:2023-04-13 Online:2023-06-15 Published:2023-06-06
  • About author:ZHANG Yaqing,born in 1999,postgraduate,is a member of China Computer Federation.Her main research interests is knowledge graph.WANG Yasha,born in 1975,Ph.D,professor.His main research interests include big data analysis,artificial intelligence,and urban computing.
  • Supported by:
    National Natural Science Foundation of China(62172011) and Fundamental Research Funds for the Central Universities.

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Abstract: With the development of big data technology,a large amount of heterogeneous data has been generated in various fields.Constructing knowledge graph is an important means to realize semantic intercommunication of heterogeneous data.It is a common method to generate instance model by matching structured data with ontology model mapping.However,most of the existing construction methods require users to manually complete all mapping matching,and the mapping operation is time-consuming and error-prone,unable to perform intelligent matching.In addition,the existing methods do not support incremental updates of the instances.This paper analyzes the existing instance construction methods,and proposes an instance construction and evolution method based on intelligent mapping recommendation to solve the problem of cumbersome manual mapping.Before manually mapping by users,the mapping reuse recommendation mechanism performs multilevel similarity calculation,including element-level similarity,table-level similarity and inter-table propagation similarity,and generates recommendation mapping according to the sorting result of matching.In addition,the incremental discovery mechanism can automatically discover redundant and conflicting instances and generate system background tasks for processing,so as to realize efficient and repeatless import of instances.Experiments are carried out on Shandong government open dataset and Shenzhen medical emergency dataset.With the help of the mapping reuse recommendation module,the interaction time is 3~4 times shorter than that of the traditional mode,and the matching accuracy of field recommendation reaches 98.1%.In the experiment of incremental discovery mechanism,the time required to import 13.94 million instance nodes and 21.58 million relationship edges is reduced from 31.21h to 2.23h,which proves the availability and matching accuracy of intelligent mapping reuse recommendation,and improves the efficiency of instance layer construction and growth.

Key words: Knowledge graph, Schema matching, Mapping reusing, Instance construction, Graph evolution

CLC Number: 

  • TP311
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