Abstract
Neural IR has advanced through two distinct paths: entity-oriented approaches leveraging knowledge graphs and multi-vector models capturing fine-grained semantics. We introduce QDER, a neural re-ranking model that unifies these approaches by integrating knowledge graph semantics into a multi-vector model. QDER's key innovation lies in its modeling of query-document relationships: rather than computing similarity scores on aggregated embeddings, we maintain individual token and entity representations throughout the ranking process, performing aggregation only at the final scoring stage-an approach we call "late aggregation." We first transform these fine-grained representations through learned attention patterns, then apply carefully chosen mathematical operations for precise matches. Experiments across five standard benchmarks show that QDER achieves significant performance gains, with improvements of 36% in nDCG@20 over the strongest baseline on TREC Robust 2004 and similar improvements on other datasets. QDER particularly excels on difficult queries, achieving an nDCG@20 of 0.70 where traditional approaches fail completely (nDCG@20 = 0.0), setting a foundation for future work in entity-aware retrieval.
Recommended Citation
S. Chatterjee and J. Dalton, "QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Ranking," SIGIR 2025 Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2255 - 2265, Association for Computing Machinery, Jul 2025.
The definitive version is available at https://doi.org/10.1145/3726302.3730065
Department(s)
Computer Science
Publication Status
Open Access
Keywords and Phrases
Multi-Vector Entity-Oriented Search; Query-Specific Embedding
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Association for Computing Machinery, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
13 Jul 2025
