面向自主工业软件的知识提取和知识库构建方法  

Knowledge extraction and knowledge base construction method from industrial software packages

作　　者：王立平[1,2] 张超 蔡恩磊 史慧杰 王冬 WANG Liping;ZHANG Chao;CAI Enlei;SHI Huijie;WANG Dong(Department of Mechanical Engineering,Tsinghua University,Beijing 100084,China;School of Mechanical and Electrical Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China)

机构地区：[1]清华大学机械工程系,北京100084 [2]电子科技大学机械与电气工程学院,成都611731

出　　处：《清华大学学报（自然科学版）》2022年第5期978-986,共9页Journal of Tsinghua University(Science and Technology)

基　　金：国家重点研发计划项目(2020YFB1712303)。

摘　　要：自主工业软件是支撑国内中小企业创新发展的核心力量之一。自主工业软件相关文本中蕴含着大量与制造业相关的知识,但是目前缺少相应的知识提取和知识库构建方法。该文提出一种基于神经网络和自然语言处理的知识提取模型,该模型包括文本表示、实体识别、关系抽取3个部分。基于知识图谱对提取的实体和关系进行建模,通过本体建模定义自主工业软件相关概念,利用图数据建模将本体模型中的概念映射到图数据中,提升了数据检索和建模能力,并将数据持久化存储到知识库中。应用结果表明:该方法可用于构建自主工业软件知识库,对整合制造业相关知识起到重要作用。Industrial software is a key force supporting the development of domestic small and medium-sized enterprises. Industrial software packages contain a large amount of knowledge related to manufacturing processes, but little of the knowledge embedded in these software packages has been extracted and put into a knowledge base. This paper presents a knowledge extraction model that combines neural networks and natural language processing. The model includes text representation, entity recognition, and relationship extraction. The extracted entities and relationships are modeled on a knowledge graph, while related concepts in the software are defined through ontology modeling. The ontology model concepts are mapped to graph data to improve data retrieval and modeling capabilities and the data can be stored in the knowledge base with long term. The results show that this method can build an industrial software knowledge base which will play an important role in integrating manufacturing knowledge.

关 键 词：自主工业软件 神经网络 实体识别 关系抽取 知识图谱 

分 类 号：TG659[金属学及工艺—金属切削加工及机床]