We present an AI-assisted search tool, the "Design Concept Exploration Graph" ("D-Graph"). It assists automotive designers in creating an original design-concept phrase, that is, a combination of two adjectives that conveys product aesthetics. D-Graph retrieves adjectives from a ConceptNet knowledge graph as nodes and visualizes them in a dynamically scalable 3D graph as users explore words. The retrieval algorithm helps in finding unique words by ruling out overused words on the basis of word frequency from a large text corpus and words that are too similar between the two in a combination using the cosine similarity from ConceptNet Numberbatch word embeddings. Our experiment with participants in the automotive design field that used both the proposed D-Graph and a baseline tool for design-concept-phrase creation tasks suggested a positive difference in participants' self-evaluation on the phrases they created, though not significant. Experts' evaluations on the phrases did not show significant differences. Negative correlations between the cosine similarity of the two words in a design-concept phrase and the experts' evaluation were significant. Our qualitative analysis suggested the directions for further development of the tool that should help users in adhering to the strategy of creating compound phrases supported by computational linguistic principles.
翻译:我们提出了一个由AI协助的搜索工具,即“设计概念探索图”(“D-Graph”)。它帮助汽车设计师创建了一个原始的设计概念短语,即两个形容词的组合,以传达产品的美学。D-Graph从概念网知识图形中提取形容词,作为节点,并以动态可缩放的3D图作为用户的探索词句。检索算法有助于找到独特的词句,在大文本文体和两个词之间过于相似的词频的基础上排除使用过多的词句,同时使用概念网编号匹配词嵌入的对等词。我们与汽车设计设计领域参与者的实验,既使用拟议的D-Graph,又使用设计-概念创建任务的基线工具,表明参与者对他们所创作的词句子的自我评价存在积极差异,尽管并不重要。专家们对词句子的评价没有显示出显著的差异。设计-网络数据库中两个词句子的相似性与两个词词组的组合,在设计-数字匹配词组嵌入的词组内嵌入词组中。我们与设计-Gravelyal 专家在设计-dealal 分析中建议的计算方法中支持进一步的用户方向的定性分析是重要的。