A Preliminary Roadmap for LLMs as Assistants in Exploring, Analyzing, and Visualizing Knowledge Graphs
arxiv(2024)
摘要
We present a mixed-methods study to explore how large language models (LLMs)
can assist users in the visual exploration and analysis of knowledge graphs
(KGs). We surveyed and interviewed 20 professionals from industry, government
laboratories, and academia who regularly work with KGs and LLMs, either
collaboratively or concurrently. Our findings show that participants
overwhelmingly want an LLM to facilitate data retrieval from KGs through joint
query construction, to identify interesting relationships in the KG through
multi-turn conversation, and to create on-demand visualizations from the KG
that enhance their trust in the LLM's outputs. To interact with an LLM,
participants strongly prefer a chat-based 'widget,' built on top of their
regular analysis workflows, with the ability to guide the LLM using their
interactions with a visualization. When viewing an LLM's outputs, participants
similarly prefer a combination of annotated visuals (e.g., subgraphs or tables
extracted from the KG) alongside summarizing text. However, participants also
expressed concerns about an LLM's ability to maintain semantic intent when
translating natural language questions into KG queries, the risk of an LLM
'hallucinating' false data from the KG, and the difficulties of engineering a
'perfect prompt.' From the analysis of our interviews, we contribute a
preliminary roadmap for the design of LLM-driven knowledge graph exploration
systems and outline future opportunities in this emergent design space.
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