From Query to Knowledge Topology: Agentic Knowledge Extraction for Multi-Perspective Graph Retrieval

User queries can be partial and potentially ambiguous descriptions of the knowledge required to answer them. Retrieval based on a single query representation can therefore overlook relevant information, relationships, and alternative interpretations that remain implicit in the question. We propose a training-free framework for multi-perspective answer-set retrieval that reconstructs a candidate query-relevant knowledge topology through iterative LLM probing and uses this structure to guide retrieval from an external knowledge graph. Building on existing agentic knowledge-extraction methods, the framework explores the query through sequential associative probing, taxonomy exploration, and multi-perspective parallel probing. Extracted knowledge is filtered for relevance and redundancy and organized into an explicit structure of information, relationships, and perspectives. This structure provides a provisional model of the query’s knowledge requirements: its perspectives guide adaptive region retrieval, while its relationships inform the construction of supporting evidence through subgraphs. Retrieved entities seed a prize-collecting procedure over knowledge-graph relations, and the resulting subgraphs are organized into perspective-specific answer sets. The LLM then synthesizes these evidence sets into an answer that presents complementary findings and evidence-supported disagreements. We evaluate the framework on FB15K-237, WN18RR, PrimeKG, and NELL-995 using retrieval accuracy and answer-set distinctness.