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    <abstract>Knowledge graphs (KGs) are increasingly used as structured context for Large Language Models (LLMs), but industrial KG-RAG systems often need to integrate public and domain-specific KGs constructed from heterogeneous databases. This integration relies on Entity Alignment (EA), where lexical matching alone is insufficient under predicate-name variation and incomplete local neighborhoods. We address EA for KG integration by constructing a pairwise EA dataset and proposing two complementary modules: Predicate Importance Estimation (PIE) and Decoupled Rationale-Score Distillation (DRSD). PIE is a compact embedding-based approach that removes the subject information from each 1-hop triple, encodes the resulting subjectless triples, and aggregates them with learnable predicate-importance weights to build predicate-aware entity embeddings. DRSD trains a distilled small language model (SLM) with pseudo-answers produced by a teacher LLM through distinct prompts. By converting binary EA labels into text-based supervision and decoupling confidence-score estimation from label-consistent rationales, DRSD enables the SLM to learn task-specific reasoning while retaining a less label-biased confidence signal. Experiments show that PIE and DRSD improve EA classification. Moreover, because DRSD decouples confidence-score estimation from the decision, a division between the two flags an uncertain prediction for human review, thereby enabling a practical discrepancy between automatic acceptance and human-in-the-loop verification.</abstract>
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%0 Conference Proceedings
%T Predicate Importance Estimation and Decoupled Rationale-Score Distillation for Entity Alignment
%A Kim, Keunha
%A Jang, Yoonjin
%A Lee, Hyeon-gu
%A Kim, Sihyung
%A Ko, Youngjoong
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F kim-etal-2026-predicate
%X Knowledge graphs (KGs) are increasingly used as structured context for Large Language Models (LLMs), but industrial KG-RAG systems often need to integrate public and domain-specific KGs constructed from heterogeneous databases. This integration relies on Entity Alignment (EA), where lexical matching alone is insufficient under predicate-name variation and incomplete local neighborhoods. We address EA for KG integration by constructing a pairwise EA dataset and proposing two complementary modules: Predicate Importance Estimation (PIE) and Decoupled Rationale-Score Distillation (DRSD). PIE is a compact embedding-based approach that removes the subject information from each 1-hop triple, encodes the resulting subjectless triples, and aggregates them with learnable predicate-importance weights to build predicate-aware entity embeddings. DRSD trains a distilled small language model (SLM) with pseudo-answers produced by a teacher LLM through distinct prompts. By converting binary EA labels into text-based supervision and decoupling confidence-score estimation from label-consistent rationales, DRSD enables the SLM to learn task-specific reasoning while retaining a less label-biased confidence signal. Experiments show that PIE and DRSD improve EA classification. Moreover, because DRSD decouples confidence-score estimation from the decision, a division between the two flags an uncertain prediction for human review, thereby enabling a practical discrepancy between automatic acceptance and human-in-the-loop verification.
%U https://aclanthology.org/2026.emnlp-industry.213/
%P 3570-3587

Markdown (Informal)

[Predicate Importance Estimation and Decoupled Rationale-Score Distillation for Entity Alignment](https://aclanthology.org/2026.emnlp-industry.213/) (Kim et al., EMNLP 2026)

• Predicate Importance Estimation and Decoupled Rationale-Score Distillation for Entity Alignment (Kim et al., EMNLP 2026)

ACL

• Keunha Kim, Yoonjin Jang, Hyeon-gu Lee, Sihyung Kim, and Youngjoong Ko. 2026. Predicate Importance Estimation and Decoupled Rationale-Score Distillation for Entity Alignment. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 3570–3587, Budapest, Hungary. Association for Computational Linguistics.