基于技术语义网络的思想生成

IF 1.7 3区 工程技术 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Serhad Sarica, Binyang Song, Jianxi Luo, K. Wood
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引用次数: 43

摘要

摘要:挖掘公共和常识性的语义网络数据库用于工程设计灵感的研究越来越多。然而,目前仍然缺乏基于语义网络数据库的设计构思辅助工具,这些数据库专门用于工程或技术知识。在这项研究中,我们提出了一种使用技术语义网络(TechNet)来激发工程设计中的想法生成的新方法。该方法的核心是在围绕焦点设计领域的空白空间中,根据其在大型TechNet中的语义距离,引导新技术概念的推理,以潜在地合成新的设计思想。我们在总体上证明了有效性,并通过飞行汽车设计创意生成的案例研究使用该方法的策略和创意结果含义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Idea generation with Technology Semantic Network
Abstract There are growing efforts to mine public and common-sense semantic network databases for engineering design ideation stimuli. However, there is still a lack of design ideation aids based on semantic network databases that are specialized in engineering or technology-based knowledge. In this study, we present a new methodology of using the Technology Semantic Network (TechNet) to stimulate idea generation in engineering design. The core of the methodology is to guide the inference of new technical concepts in the white space surrounding a focal design domain according to their semantic distance in the large TechNet, for potential syntheses into new design ideas. We demonstrate the effectiveness in general, and use strategies and ideation outcome implications of the methodology via a case study of flying car design idea generation.
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来源期刊
CiteScore
4.40
自引率
14.30%
发文量
27
审稿时长
>12 weeks
期刊介绍: The journal publishes original articles about significant AI theory and applications based on the most up-to-date research in all branches and phases of engineering. Suitable topics include: analysis and evaluation; selection; configuration and design; manufacturing and assembly; and concurrent engineering. Specifically, the journal is interested in the use of AI in planning, design, analysis, simulation, qualitative reasoning, spatial reasoning and graphics, manufacturing, assembly, process planning, scheduling, numerical analysis, optimization, distributed systems, multi-agent applications, cooperation, cognitive modeling, learning and creativity. AI EDAM is also interested in original, major applications of state-of-the-art knowledge-based techniques to important engineering problems.
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