An automatic method for constructing machining process knowledge base from knowledge graph. (February 2022)

• Record Type: Journal Article
• Authors: Guo, Liang; Yan, Fu; Li, Tian; Yang, Tao; Lu, Yuqian
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 73(2022)
• Publication Date: 2022-02
• Subjects: Process Knowledge Base -- Knowledge Graph -- Fuzzy Evaluation -- NLP; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2021.102222
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: A framework for automatically constructing a knowledge base is developed. The extraction effect of this framework is better than other frameworks. A evaluation algorithm is proposed to judge the optimal expression of knowledge. Semantic and attribute weighting factors among knowledge entities are considered. Abstract: The process knowledge base is the key module in intelligent process design, it determines the intelligence degree of the design system and affects the quality of product design. However, traditional process knowledge base construction is non-automated, time consuming and requires much manual work, which is not sufficient to meet the demands of the modern manufacturing mode. Moreover, the knowledge base often adopts a single knowledge representation, and this may lead to ambiguity in the meaning of some knowledge, which will affect the quality of the process knowledge base. To overcome the above problems, an automatic construction framework for the process knowledge base in the field of machining based on knowledge graph (KG) is introduced. First, the knowledge is classified and annotated based on the function-behavior-states (FBS) design method. Second, a knowledge extraction framework based on BERT-BiLSTM-CRF is established to perform the automatic knowledge extraction of process text. Third, a knowledge representation method based on fuzzy comprehensive evaluation is established, forming three types of knowledge representation with the KG as the main, production rules and two-dimensional data linked list as a supplement. In addition, to overcome the redundancy in the knowledge fusion stage, a hybrid algorithm based on an improved edit distance and attribute weighting is built. Finally, a prototype system is developed, and quality analysis is carried out. Compared with the F values of BiLSTM-CRF and CNN-BiLSTM-CRF, that of the proposed extraction method in the machining domain is increased by 7.35% and 3.87%, respectively.