PatentMiner：基于上下文增强和知识引导的图注意力的专利空缺挖掘
PatentMiner: Patent Vacancy Mining via Context-enhanced and Knowledge-guided Graph Attention
摘要：
虽然有少量的工作是通过构建知识图谱来进行专利研究，但是没有利用专利文献构建专利知识图谱，结合最新的自然语言处理方法来挖掘现有专利中隐藏的丰富语义关系，并预测新的可能专利。提出一种基于知识图谱和图注意力机制的专利空缺预测方法PatentMiner，挖掘丰富的语义知识，预测新的潜在专利。首先，通过对专利文献进行命名实体识别和关系提取，构建了随时间（如年份）变化的专利知识图谱。其次，提出了公共邻居方法（CNM）、图注意力网络（GAT）和上下文增强图注意力网络（CGAT），在构建的知识图谱中进行链接预测，挖掘出潜在的三元组。最后，利用同现关系将专利定义在知识图谱上，即每个专利表示为一个全连通子图，该子图包含了该专利在知识图谱中的所有实体和同现关系;并提出了一种新的专利预测任务，将新增加预测链接的全连通子图预测为新专利。实验结果表明，本文提出的专利预测方法能够准确地预测新专利，且上下文增强图注意力网络的预测效果明显优于基线。同时，我们提出的专利空缺预测任务还有很大的改进空间。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
