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.

设计人员通常面临从大量非结构化文本文档中查找信息以准备做出决定的问题。主要的挑战是嵌入在文本文档中的知识很难在语义级别进行搜索，因此无法及时支持决策。为了应对这一挑战，在本文中，我们提出了一种基于知识图的方法，用于在工程设计中集成和导航与决策相关的知识。所提出的方法基于决策知识图 (mDKG) 元模型，该模型基于折衷决策支持问题 (cDSP) 构造，设计人员将其用作在语言和数学上制定设计决策的一种手段。基于 mDKG，我们提出了一种通过自然语言处理将基于词的 cDSP 自动转换为知识图谱的程序，以及一种通过发散和收敛过程快速准确地导航决策相关知识的程序。使用来自供应链设计领域的文本数据验证基于知识图的方法。结果表明，在查找目标知识的有效性和效率方面，我们的方法比传统的基于关键字的搜索方法具有更好的性能。