arXiv：cs.AI· Junran Yang, Shruti Badrish, Teanna Barrett, Leilani Battle·· 3 小时前
DAG-EDA：用意图图谱导航探索性数据分析的推理空间
Intent Graph: Navigating the Analytical Reasoning Space for Exploratory Data Analysis
AI 导读
研究者提出 DAG-EDA 系统，让分析师与 LLM 通过两个联动结构共同导航可能的分析空间。其中意图图谱按分析意图语法将模糊的自然语言问题逐步分解为具体分析任务，支持分支、回溯与路径比较；多层知识图谱则外化 LLM 的领域知识，把领域概念与可测量它的数据集变量关联起来。两个图谱仅由数据集与分析师的问题构建，所得分析以交互式仪表盘呈现，论文还描述了用于检验其是否支撑分析师推理与导航的用户研究设计。
正文
Abstract:Exploratory data analysis (EDA) is rarely open-ended in practice: analysts work from high-level domain questions toward the concrete analyses that can answer them, prioritizing directions with domain knowledge and prior hypotheses. Large language models (LLMs) can supply such knowledge, but their responses are unstructured, leaving analysts no way to see what has been explored, what is missing, or why one direction was chosen over another. We present DAG-EDA, a system that lets analysts and an LLM co-navigate the space of possible analyses through two linked structures. An intent graph, governed by a grammar of analytical intent, decomposes an ambiguous natural-language question into progressively concrete analysis tasks, keeping alternative framings open and letting analysts branch, backtrack, and compare paths. A multi-layered knowledge graph externalizes the LLM's domain knowledge, linking domain concepts to the dataset variables that can measure them, so analysts can inspect and contest how their question is grounded in the data. Both graphs are constructed from only the dataset and the analyst's question, and the analyses the analyst reaches are rendered as interactive dashboards. We illustrate the system through a usage scenario and describe a user study design for examining whether the system scaffold analysts' reasoning and navigation.
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.11025 [cs.HC] |
| (or arXiv:2610.11025v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11025 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Junran Yang [view email]
[v1]
Thu, 8 Oct 2026 00:19:01 UTC (2,267 KB)
来源：arXiv：cs.AI · arxiv.org