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arXiv · 2504.10613

Enhancing Document Retrieval for Curating N-ary Relations in Knowledge Bases

Xing David Wang·Ulf Leser
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Abstract

Curation of biomedical knowledge bases (KBs) relies on extracting accurate multi-entity relational facts from the literature - a process that remains largely manual and expert-driven. An essential step in this workflow is retrieving documents that can support or complete partially observed n-ary relations. We present a neural retrieval model designed to assist KB curation by identifying documents that help fill in missing relation arguments and provide relevant contextual evidence. To reduce dependence on scarce gold-standard training data, we exploit existing KB records to construct weakly supervised training sets. Our approach introduces two key technical contributions: (i) a layered contrastive loss that enables learning from noisy and incomplete relational structures, and (ii) a balanced sampling strategy that generates high-quality negatives from diverse KB records. On two biomedical retrieval benchmarks, our approach achieves state-of-the-art performance, outperforming strong baselines in NDCG@10 by 5.7 and 3.7 percentage points, respectively.

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Xing David Wang, Ulf Leser. 2025-04-14. Enhancing Document Retrieval for Curating N-ary Relations in Knowledge Bases. https://arxiv.org/abs/2504.10613

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