Modelling and Implementation of a Knowledge Question-answering System for Product Quality Problem Based on Knowledge Graph

Aiming at the problem of difficulty in understanding the semantics of the problem in the traditional quality problem management system, the knowledge retrieval technology of product quality problem based on the knowledge graph is carried out. The process model for knowledge retrieval of quality prob...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-10, Vol.2068 (1), p.12051
Main Authors: Liu, Hanxu, Dong, Fangxu, Wang, Meiqing, Lin, Qiu
Format: Article
Language:English
Subjects:
knowledge graph
naive bayes classifier
natural language retrieval
Quality problem
semantic templates
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Summary:Aiming at the problem of difficulty in understanding the semantics of the problem in the traditional quality problem management system, the knowledge retrieval technology of product quality problem based on the knowledge graph is carried out. The process model for knowledge retrieval of quality problem based on semantic templates is constructed. A domain corpus is built, which consisting of thousands of quality problem handling records. The TF-IDF (Term Frequency-inverse Document Frequency) algorithm was used to extracted the vocabulary from the quality problem analysis reports. A natural language question semantic classification process model based on Naive Bayes classifier is established to improve the accuracy of semantic template matching. On the basis of theoretical study, a quality problem knowledge question-answering system-QQ-KQAS based on knowledge graph is developed, and the effectiveness of the proposed method is verified through examples.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2068/1/012051