Integrating and navigating engineering design decision-related knowledge using decision knowledge graph

Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not read...

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Bibliographic Details
Published inAdvanced engineering informatics Vol. 50; p. 101366
Main Authors Hao, Jia, Zhao, Lei, Milisavljevic-Syed, Jelena, Ming, Zhenjun
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.10.2021
Subjects
Decision support
Design
Knowledge graph
Navigation
Searching
Design
Navigation
Searching
Decision support
Knowledge graph
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Summary:Designers are usually facing a problem of finding information from a huge amount of unstructured textual documents in order to prepare for a decision to be made. The major challenge is that knowledge embedded in the textual documents are difficult to search at a semantic level and therefore not ready to support decisions in a timely manner. To address this challenge, in this paper we propose a knowledge-graph-based method for integrating and navigating decision-related knowledge in engineering design. The presented method is based on a meta-model of decision knowledge graph (mDKG) that is grounded in the compromise Decision Support Problem (cDSP) construct which is used by designers as a means to formulate design decisions linguistically and mathematically. Based on the mDKG, we propose a procedure for automatically converting word-based cDSPs to knowledge graph through natural language processing, and a procedure for rapidly and accurately navigating decision-related knowledge through divergence and convergence processes. The knowledge-graph-based method is verified using the textual data from the supply chain design domain. Results show that our method has better performance than the conventional keyword-based searching method in terms of both effectiveness and efficiency in finding the target knowledge.
ISSN:1474-0346
1873-5320
DOI:10.1016/j.aei.2021.101366