Novel semantic retrieval approach for semi-structured knowledge in industrial software development

被引：0
|
作者：
Wang C. [1 ]
Jiang Z. [1 ]
Wang F. [1 ]
Ji Y. [1 ]
Jiang H. [2 ]
机构：
[1] School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai
[2] Shanghai Hypers Data Technology Inc., Shanghai
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2021年 / 27卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Data mining; Hypernetwork model; Knowledge management; Knowledge retrieval; Semantic search;
D O I：
10.13196/j.cims.2021.08.019
中图分类号：
学科分类号：
摘要：
In knowledge-driven industrial software development, assisting engineers in searching heterogeneous semi-structured knowledge efficiently and accurately is a major issue. A semantic retrieval method was proposed based on the knowledge super network model. The knowledge super network consisting of product subnet, object subnet, and knowledge subnet was built with the relations between the concepts of code reuse and the attributes of engineering knowledge. To calculate the process context correlation between user query and engineering knowledge, the conceptual knowledge and language model were integrated by Bayesian method. Experimental results on Microsoft knowledge base dataset show that the proposed approach could improve the precision of knowledge retrieval comparing to several semantic retrieval methods. The feasibility and effectiveness of the approach were also verified. © 2021, Editorial Department of CIMS. All right reserved.
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页码：2371 / 2381
页数：10
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