面向自主工业软件的知识提取和知识库构建方法

王立平; 张超; 蔡恩磊; 史慧杰; 王冬

doi:10.16511/j.cnki.qhdxxb.2022.22.023

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清华大学学报（自然科学版） ›› 2022, Vol. 62 ›› Issue (5) : 978-986. DOI: 10.16511/j.cnki.qhdxxb.2022.22.023

机械工程

面向自主工业软件的知识提取和知识库构建方法

王立平^1,2, 张超², 蔡恩磊², 史慧杰², 王冬¹

作者信息 +

Knowledge extraction and knowledge base construction method from industrial software packages

WANG Liping^1,2, ZHANG Chao², CAI Enlei², SHI Huijie², WANG Dong¹

Author information +

文章历史 +

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。

Abstract

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.

导出引用

王立平, 张超, 蔡恩磊, 史慧杰, 王冬. 面向自主工业软件的知识提取和知识库构建方法[J]. 清华大学学报（自然科学版）. 2022, 62(5): 978-986 https://doi.org/10.16511/j.cnki.qhdxxb.2022.22.023

WANG Liping, ZHANG Chao, CAI Enlei, SHI Huijie, WANG Dong. Knowledge extraction and knowledge base construction method from industrial software packages[J]. Journal of Tsinghua University(Science and Technology). 2022, 62(5): 978-986 https://doi.org/10.16511/j.cnki.qhdxxb.2022.22.023

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基金

国家重点研发计划项目（2020YFB1712303）

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收稿日期	出版日期
2021-12-13	2022-05-15
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2022-04-26

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