Knowledge Graph-based Patent Clustering
Lai, Pei-Yuan Chen, Man-Sheng Dai, Qing-Yun Wang, Chang-Dong Chen, Min Guizani, Mohsen
Lai, Pei-Yuan
Chen, Man-Sheng
Dai, Qing-Yun
Wang, Chang-Dong
Chen, Min
Guizani, Mohsen
Supervisor
Department
Machine Learning
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Type
Journal article
Date
2025
License
Language
English
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Research Projects
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Abstract
Patent data generally includes information from different perspectives or different types, and its heterogeneous attributes can be greatly beneficial to data clustering analysis. However, the existing patent analysis method always focus on the patent text cues, and such a strategy merely depends on the feature information to capture the data characteristics, failing to multi-type informative patent representation. Therefore, in this paper, to model the underlying structure/relationships of patent data, we employ the knowledge graph to depict the heterogeneous attributes of patent, and propose a novel Knowledge Graph-based Patent Clustering (KGPC) method, where the relationship reconstruction in knowledge graph as well as clustering-oriented representation refinement for patent clustering are jointly considered. With this model, there are three components, i.e., entity representation refinement, relationship reconstruction and self-supervised entity clustering. Given a patent knowledge graph as input, the entity representation refinement can be mutually boosted by the relationship reconstruction and self-supervised clustering objective, thereby leading to a balanced clustering-oriented output. Extensive experiments on several real-world patent knowledge graph datasets validate the effectiveness of KGPC while compared with the state-of-the-art.
Citation
P. -Y. Lai, M. -S. Chen, Q. -Y. Dai, C. -D. Wang, M. Chen and M. Guizani, "Knowledge Graph-based Patent Clustering," in IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2025.3590406
Source
IEEE Transactions on Knowledge and Data Engineering
Conference
Keywords
Patent clustering, knowledge graph, representation refinement, relationship reconstruction, self-supervised clustering
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Source
Publisher
IEEE