Extracting Document Relations from Search Corpus by Marginalizing over User Queries

• URL: http://arxiv.org/abs/2507.10726v1
• Date: Mon, 14 Jul 2025 18:47:13 GMT
• Title: Extracting Document Relations from Search Corpus by Marginalizing over User Queries
• Authors: Yuki Iwamoto, Kaoru Tsunoda, Ken Kaneiwa,
• Abstract summary: We propose a novel framework that discovers document relationships through query marginalization.<n>Extracting Document Relations by Marginalizing over User queries is based on the insight that strongly related documents often co-occur in diverse user queries.<n>Our query-driven framework offers a practical approach to document organization that adapts to different user perspectives and information needs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding relationships between documents in large-scale corpora is essential for knowledge discovery and information organization. However, existing approaches rely heavily on manual annotation or predefined relationship taxonomies. We propose EDR-MQ (Extracting Document Relations by Marginalizing over User Queries), a novel framework that discovers document relationships through query marginalization. EDR-MQ is based on the insight that strongly related documents often co-occur in results across diverse user queries, enabling us to estimate joint probabilities between document pairs by marginalizing over a collection of queries. To enable this query marginalization approach, we develop Multiply Conditioned Retrieval-Augmented Generation (MC-RAG), which employs conditional retrieval where subsequent document retrievals depend on previously retrieved content. By observing co-occurrence patterns across diverse queries, EDR-MQ estimates joint probabilities between document pairs without requiring labeled training data or predefined taxonomies. Experimental results show that our query marginalization approach successfully identifies meaningful document relationships, revealing topical clusters, evidence chains, and cross-domain connections that are not apparent through traditional similarity-based methods. Our query-driven framework offers a practical approach to document organization that adapts to different user perspectives and information needs.

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