Semantic Bundling: Interactive Node and Edge Bundling to Simplify Knowledge Graphs using Large Language Models

2026-08-04

AI Summary

This work addresses the “hairball problem” in large-scale knowledge graphs, where dense nodes and relations obscure semantic information and hinder interpretability. To tackle this challenge, the authors propose an interactive semantic aggregation method powered by large language models (LLMs), enabling user-driven compression of graph structures. The approach dynamically aggregates local subgraphs into interpretable super-nodes and super-edges while preserving traceability to original triples and source documents. By integrating LLMs, knowledge graph construction, and interactive visualization, the method effectively surfaces high-level insights from document collections in applications such as movie review analysis and intelligence assessment, significantly enhancing both the comprehensibility and explainability of complex knowledge graphs.

Abstract

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.

Problem

• knowledge graphs
• hairball problem
• semantic interpretation
• visual analytics
• document corpora

Innovation

• Semantic Bundling
• Knowledge Graphs
• Large Language Models
• Visual Analytics
• Super Nodes