人机协作构建动态知识图谱，提升信息整合效率
MindTrellis: Co-Creating Knowledge Structures with AI through Interactive Visual Exploration
- 用户与AI共同构建可交互的知识图谱，支持概念增删和关系调整
- 实测显示知识组织质量优于纯检索系统，认知负担更低
- 适合需要深度整合多源信息的科研、写作等知识工作者
知识工作者在将多文档信息整合为结构化认知时面临挑战。这一过程具有迭代性：用户探索内容、发现概念间关系，并不断重构心智模型。现有方法支持有限：大模型系统允许查询但无法塑造知识结构；手动工具如思维导图支持结构创建却缺乏智能辅助。为此，我们提出MindTrellis，一个用户与AI协同构建动态知识图谱的交互式可视化系统。用户可查询图谱获取文档支撑信息，并通过引入新概念、修改关系、调整层级来反映理解演进。12名参与者在制作演示文稿的任务中，使用MindTrellis的团队在专家评估的内容覆盖度与结构质量上均优于仅支持检索的基线系统。
原文摘要 · Abstract (English)
Knowledge workers face increasing challenges in synthesizing information from multiple documents into structured conceptual understanding. This process is inherently iterative: users explore content, identify relationships between concepts, and continuously reorganize their mental models. However, current approaches offer limited support. LLM-based systems let users query information but not shape how knowledge is organized; manual tools like mind maps support structure creation but lack intelligent assistance. This leaves an open opportunity: supporting collaborative construction where users and AI jointly develop an evolving knowledge representation. We present MindTrellis, an interactive visual system where users and AI collaboratively build a dynamic knowledge graph. Users can query the graph to retrieve document-grounded information, and contribute by introducing new concepts, modifying relationships, and reorganizing the hierarchy to reflect their developing understanding. In a user study where 12 participants created slide decks, MindTrellis outperformed retrieval-only baselines in knowledge organization and cognitive load, as measured by expert ratings of content coverage and structural quality.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。