Open AccessModelling and Implementation of a Knowledge Question-answering System for Product Quality Problem Based on Knowledge Graph
Author(s) -
Hanxu Liu,
Dong Fang-xu,
Meiqing Wang,
Qiu Lin
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2068/1/012051
Subject(s) - computer science , question answering , domain knowledge , artificial intelligence , information retrieval , graph , vocabulary , natural language processing , data mining , theoretical computer science , philosophy , linguistics
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.