PatentMiner: Patent Vacancy Mining via Context-enhanced and Knowledge-guided Graph Attention

• URL: http://arxiv.org/abs/2107.04880v1
• Date: Sat, 10 Jul 2021 17:34:57 GMT
• Title: PatentMiner: Patent Vacancy Mining via Context-enhanced and Knowledge-guided Graph Attention
• Authors: Gaochen Wu, Bin Xu, Yuxin Qin, Fei Kong, Bangchang Liu, Hongwen Zhao, Dejie Chang
• Abstract summary: We propose a new patent vacancy prediction approach named PatentMiner to mine rich semantic knowledge and predict new potential patents. Patent knowledge graph over time (e.g. year) is constructed by carrying out named entity recognition and relation extrac-tion from patent documents. Common Neighbor Method (CNM), Graph Attention Networks (GAT) and Context-enhanced Graph Attention Networks (CGAT) are proposed to perform link prediction in the constructed knowledge graph.
• Score: 2.9290732102216452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although there are a small number of work to conduct patent research by building knowledge graph, but without constructing patent knowledge graph using patent documents and combining latest natural language processing methods to mine hidden rich semantic relationships in existing patents and predict new possible patents. In this paper, we propose a new patent vacancy prediction approach named PatentMiner to mine rich semantic knowledge and predict new potential patents based on knowledge graph (KG) and graph attention mechanism. Firstly, patent knowledge graph over time (e.g. year) is constructed by carrying out named entity recognition and relation extrac-tion from patent documents. Secondly, Common Neighbor Method (CNM), Graph Attention Networks (GAT) and Context-enhanced Graph Attention Networks (CGAT) are proposed to perform link prediction in the constructed knowledge graph to dig out the potential triples. Finally, patents are defined on the knowledge graph by means of co-occurrence relationship, that is, each patent is represented as a fully connected subgraph containing all its entities and co-occurrence relationships of the patent in the knowledge graph; Furthermore, we propose a new patent prediction task which predicts a fully connected subgraph with newly added prediction links as a new pa-tent. The experimental results demonstrate that our proposed patent predic-tion approach can correctly predict new patents and Context-enhanced Graph Attention Networks is much better than the baseline. Meanwhile, our proposed patent vacancy prediction task still has significant room to im-prove.

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