AI 中文总结
该研究提出基于LLM的语义捆绑技术，在开源系统AgentK中实现，用于简化知识图谱，通过节点边捆绑形成高级结构，在电影评论等场景中可揭示文档集合新见解。
AI 中文摘要
我们提出了语义捆绑（Semantic Bundling），这是一种用于理解以知识图谱（KG）形式表示的文本文档的可视化分析技术。将文档语料库表示为知识图谱可明确实体间的关系，使知识图谱既适用于直接分析，也适用于包括机器学习管道和生成式人工智能后端在内的计算工作流。然而，随着知识图谱规模增大，它们会变得难以针对特定任务进行解释和可视化（例如“毛线球问题”），且每种关系的含义往往隐藏在密集的源文本中。语义捆绑利用大语言模型（LLM）支持用户驱动的知识图谱中节点与边的捆绑，形成更高级别的图结构：超级节点（用于折叠和总结图的一个区域）和超级边（用于总结两个实体之间的连接）。结果与底层三元组和源文档相关联，使总结基于证据。我们在AgentK中实现了语义捆绑，这是一个开源系统，可从文本文档构建知识图谱并将图交互映射为捆绑操作。通过在电影评论和情报分析场景中的用例，我们展示了语义捆绑如何揭示文档集合中的新见解，并将我们的发现综合为关于知识图谱理解领域新兴挑战与机遇的讨论。
英文摘要
We present Semantic Bundling, a visual analytics technique for making sense of text documents represented as knowledge graphs (KGs). Representing a document corpus as a KG makes relationships between entities explicit, making KGs useful both to analyze directly and in computational workflows including ML pipelines and generative AI backends. However, as KGs grow they become difficult to interpret and visualize for specific tasks (e.g., the ``hairball problem''), with the meaning of each relationship often buried in dense source text. Semantic Bundling uses large language models (LLMs) to support user-driven bundling of nodes and edges in a KG into higher-level graph structures: super nodes, which collapse and summarize a region of the graph, and super edges, which summarize the connection between two entities. Results are linked to underlying triples and source documents, grounding summaries in evidence. We implement Semantic Bundling in AgentK, an open-source system that builds a KG from text documents and maps graph interactions to bundling operations. Through use cases on movie reviews and an intelligence analysis scenario, we show how Semantic Bundling reveals new insights in document collections, and synthesize our findings into a discussion of emerging challenges and opportunities in knowledge graph sensemaking.
CommentsUnder review. 12 pages, 13 figures
