Title: Narrative Knowledge Weaver: A Multi-Agent Framework for Knowledge Graph Construction and Analysis from Complex Narratives
Primary area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)
Keywords: Knowledge, Graph, LLM Agent, Retrieval-Augmented Generation, Multi-Agent Systems

Abstract: Long-form narratives such as screenplays and novels present rich but challenging material for knowledge extraction: characters evolve, events intertwine, and causal chains unfold across hundreds of pages. Existing large language model (LLM) approaches to knowledge graph construction often capture only surface-level entities and relations, leaving narrative structures underexplored. We introduce Narrative Knowledge Weaver, a multi-agent framework that integrates schema induction, reflection-driven extraction, and event-centric refinement to build coherent graphs from complex narratives. Our system leverages graph probing to induce task-specific schemas, employs reflection loops for stable entity and relation extraction, and promotes events into structured representations that support causal and plot-level analysis. We evaluate Narrative Knowledge Weaver across NarrativeQA and a complementary collection of practitioner-style screenplays, showing consistent improvements in entity disambiguation, relation accuracy, and event causality inference over strong baselines. Beyond quantitative gains, the resulting graphs enable interpretable queries about narrative dynamics—from character states to causal chains—demonstrating how LLM-based graph construction can transform long-form stories into structured, analyzable knowledge.

TL;DR: Narrative Knowledge Weaver: a multi-agent framework that builds narrative knowledge graphs with schema induction, disambiguation, and event-centric refinement, boosting QA over screenplays and novels.