D-Graph: AI-Assisted Design Concept Exploration Graph
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.
Code (0)
등록된 구현이 없습니다.
Tasks
RetrievalWord EmbeddingsSimilar Papers 제목 키워드 기반
Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph
Current AI-assisted programming tools are predominantly linear and chat-based, which deviates from the iterative and branching nature of programming itself. Our preliminary study with developers using AI assistants sugge…
The Role of Visualization in LLM-Assisted Knowledge Graph Systems: Effects on User Trust, Exploration, and Workflows
Knowledge graphs (KGs) are powerful data structures, but exploring them effectively remains difficult for even expert users. Large language models (LLMs) are increasingly used to address this gap, yet little is known emp…
Knowledge GraphsDisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis
Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good results, but their student, exercise, and concept representations are learned and exchanged in an implicit unified graph, which mak…
cognitive diagnosisDiagnosticGraph AttentionGraph LearningGraph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination
Accelerating materials discovery requires AI systems that can generate scientifically valid hypotheses through multi-step, domain-grounded reasoning. Standard large language models often produce fluent but weakly traceab…
Reinforcement Learning"The Diagram is like Guardrails": Structuring GenAI-assisted Hypotheses Exploration with an Interactive Shared Representation
Data analysis encompasses a spectrum of tasks, from high-level conceptual reasoning to lower-level execution. While AI-powered tools increasingly support execution tasks, there remains a need for intelligent assistance i…