Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11): 185-196.doi: 10.11896/jsjkx.211100063

• Artificial Intelligence • Previous Articles     Next Articles

Methods of Patent Knowledge Graph Construction

DENG Liang1,2,3, CAO Cun-gen4   

  1. 1 School of Computer Science and Technology,University of Chinese Academy of Sciences,Beijing 100049,China
    2 Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China
    3 Patent Office,China National Intellectual Property Administration,Beijing 100083,China
    4 Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2021-11-05 Revised:2022-03-11 Online:2022-11-15 Published:2022-11-03
  • About author:DENG Liang,born in 1980,postgra-duate.His main research interests include deep learning and knowledge graph.
    CAO Cun-gen,born in 1964,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include large-scale knowledge process and so on.

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Abstract: Patent knowledge graph plays a important role in patent accurate retrieval,patent in-depth analysis and patent know-ledge training.This paper proposes a practical patent knowledge graph construction method based on seed knowledge graph,text mining and relationship completion.In this method,to ensure the quality,a seed patent knowledge graph is first established ma-nually,then the concept and relation extraction method of patent text pattern is used to expand the seed patent knowledge graph,and finally the extended patent knowledge graph is quantitatively evaluated.In this paper,artificial extraction of seed knowledge and manual summarization of lexical and syntactic patterns are carried out for patents in the field of traditional Chinese medicine.After obtaining new lexical and syntactic patterns by machine learning,the knowledge graph of seed patent is expanded and completed.Experimental results show that the number of nodes and relationships in the knowledge graph of traditional Chinese medicine are 19 453 and 194 775 respectively.After expansion,they reach 558 461 and 7 275 958 respectively,representing an increase of 27.7 and 36.3 folds respectively.

Key words: Patent text, Patent knowledge graph, Lexical and syntactic analysis, Representation learning

CLC Number: 

  • TP391
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