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Xun Zhang

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Proceedings Article | 11 September 2023 Open Access

Construction and application of power grid Patent Knowledge map

Zhicheng Ma, Jinxiong Zhao, Xun Zhang, Siheng Zhai, Baohui Wang, Guangyuan Zheng, Henan Zhang

Proceedings Volume 12779, 1277926 (2023) https://doi.org/10.1117/12.2688885

KEYWORDS: Patents, Control systems, Power grids, Data modeling, Semantics, Batteries, Data storage, Mathematical optimization, Education and training, Databases

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At present, scientific and technological personnel in electric power enterprises mainly rely on patent search websites to obtain cutting-edge electric patent information. But these websites are mainly based on string matching, which fails to capture the connection between patents, making the recommend result unsatisfying. In view of the above problems, we first constructed a grid patent knowledge map, where knowledge extraction was carried out for entities such as title, abstract and applicant in the patent text, and the entity and defined relational data were stored in the Neo4j graph database. Secondly, the Transe-SNS algorithm with optimized negative sampling was used for vectorization of the graph entity relationship. Experiments showed that Mean Rank and hit@10 improved by 27.5 and 2.2% respectively compared with the traditional TransE algorithm. Finally, the similarity between patents was calculated by combining the results of knowledge graph vector embedding of patent entities with the word vector embedding of patent title abstraction, and top- k patents in similar fields were recommended for users. The experiment proved that the proposed method is superior to the traditional text embedding method in recommending similar patent technology fields.

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