The paper introduces a new entity alignment foundation model that overcomes the limitations of existing models by addressing the "reasoning horizon gap". It employs a parallel encoding strategy that uses seed entity pairs as local anchors to guide message passing, thereby shortening inference paths and improving alignment across sparse, heterogeneous knowledge graphs. The model also incorporates a merged relation graph and a learnable interaction module, and experimental results demonstrate its strong generalizability to unseen knowledge graphs.
By Yuanning Cui, Zequn Sun, Wei Hu, Kexuan Xin, Zhangjie Fu
arXiv:2606. 06109v1 Announce Type: cross Abstract: Entity alignment (EA) aims to identify equivalent entities across heterogeneous knowledge graphs (KGs) and is a key component of knowledge fusion and cross-KG reasoning.
By Xingyu Chen, Yuanning Cui, Zequn Sun, Wei Hu
arXiv:2607. 25579v1 Announce Type: cross Abstract: Entity alignment (EA) identifies entities across knowledge graphs (KGs) that refer to the same real-world object.
By Xinran Liu, Shengtao Li, Shouqian Shi, Ge Wang, Xin-Wei Yao
arXiv:2607. 24688v1 Announce Type: cross Abstract: Entity matching identifies records that refer to the same real-world entity.
By Zeyu Zhang, Xue Li, Iacer Calixto, Paul Groth, Sebastian Schelter
arXiv:2607. 26298v1 Announce Type: new Abstract: We built and evaluated a self-serve entity resolution (ER) system on six benchmarks spanning 864 to 5M records, and three lessons emerged that are absent from existing ER literature.
By Kaushik Pavani, Ganga Aluri, Pravin Jadhav, Neeraj Prasad, Kiran Sanka
arXiv:2607. 17668v1 Announce Type: cross Abstract: Unsupervised Graph Domain Adaptation (UGDA) aims to facilitate knowledge transfer from a labeled source graph to an unlabeled target graph by mitigating cross-domain distribution shifts.
By Ridong Han, Yawen Shen, Zhongnian Li, Tongfeng Sun, Xinzheng Xu, Abdulmotaleb El Saddik
arXiv:2407. 21311v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data.
By Ali Abedi, Q. M. Jonathan Wu, Ning Zhang, Farhad Pourpanah
OpenSanctions Pairs is the first large‑scale public benchmark for entity matching on sanctions and OSINT data, comprising 755,540 expert‑labeled pairs drawn from over 1 million entities across 293 source datasets and 45 jurisdictions. The dataset spans multiple languages and writing systems, inconsistent structures, and time‑varying provenance, making it far more heterogeneous than prior benchmarks. Baseline experiments show a rule‑based matcher achieving 91.3 % F1, GPT‑4o reaching 99.0 % F1, and a locally deployable open‑source model scoring 98.2 % F1, with complementary failure modes that highlight the need to focus on downstream pipeline components.
By Chandler Smith, Magnus Sesodia, Friedrich Lindenberg, Christian Schroeder de Witt
arXiv:2607. 03154v1 Announce Type: cross Abstract: Multi-domain knowledge graph completion (MKGC) aims to improve missing triple prediction in a target KG by transferring knowledge from other support KGs.
By Jiawei Sheng, Taoyu Su, Xixun Lin, Xiaodong Li, Tingwen Liu
arXiv:2608. 15255v1 Announce Type: new Abstract: Domain modeling plays an essential role in domain-driven design, capturing essential entities and their relationships within a specific domain.
By Vasiliy Seibert
arXiv:2607. 10771v1 Announce Type: cross Abstract: Matching dependency is a generalization of the functional dependency concept, which allows users to apply custom similarity functions for matching individual attributes.
By Alexey Shlyonskikh, Michael Sinelnikov, Daniil Nikolaev, Yurii Litvinov, George Chernishev
arXiv:2509. 11819v2 Announce Type: replace Abstract: Federated Domain Adaptation (FDA) is a federated learning (FL) approach that improves model performance at the target client by collaborating with source clients while preserving data privacy.
By Mrinmay Sen, Ankita Das, Sidhant Nair, C Krishna Mohan