Computer Science ›› 2022, Vol. 49 ›› Issue (9): 215-220.doi: 10.11896/jsjkx.210700190

• Artificial Intelligence • Previous Articles     Next Articles

Ontology Alignment Method Based on Self-attention

WU Zi-yi, LI Shao-mei, JIANG Meng-han, ZHANG Jian-peng   

  1. National Digital Switching System Engineering &Technological R & D Center,Zhengzhou 450002,China
  • Received:2021-07-19 Revised:2022-02-28 Online:2022-09-15 Published:2022-09-09
  • About author:WU Zi-yi,born in 1998,master.Her main research interests include know-ledge graph and NLP.
    LI Shao-mei,born in 1982,Ph.D,asso-ciate professor,master supervisor.Her main research interests include know-ledge graph and NLP.
  • Supported by:
    Young Scientists Fund of the National Natural Science Foundation of China(62002384) and Zhengzhou Collaborative Innovation Major Project(162/32410218).

Abstract: With the development of knowledge graph in the field of artificial intelligence,there is an increasing demand to integrate knowledge graph from different sources to obtain a big knowledge graph with wider coverage.Ontology is the superstructure that can guide the construction of knowledge graph.To solve the problem of ontology alignment in knowledge graph fusion,this paper proposes an ontology alignment method based on self-attention model to combine multidimensional similarities.Firstly,two concepts from two ontologies are multi-dimensional measured by string-based,semantic-based and structure-based similarities.Then,self-attention model is used to combine above similarity calculations to judge whether the two concepts are similar or not and align them.Experiments on public datasets show that,compared with existing ontology alignment methods,the proposed method can obtain better alignment results by aggregating multi-dimensional similarity features.

Key words: Knowledge graph fusion, Ontology alignment, Similarity calculation, Self-attention model

CLC Number: 

  • TP391.1
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