Abstract
Entity alignment is an important task in Knowledge Graph(KG), which aims to find identical entities in two different KGs. Existing methods include two steps, graph representation and alignment inference. The representation is learned based on the semantics and structure of KG. In applications, however, incorrect triples (which are also called structure noise) inevitably exist in KGs due to low-quality corpora and low-performance construction algorithms. The structure noise in KGs affects the representation of KGs and the alignment inference. To this end, we propose an entity alignment method in noisy knowledge graphs for the first time. Firstly, a noise-aware module is designed to recognize the noisy triples and exclude them from KG representation. Secondly, we design a more strict semi-supervised algorithm that combines local similarity and global alignment cost together to obtain high-quality pseudo-alignments in noisy environments. The experimental results demonstrate the effectiveness of our method in noisy KGs and the good compatibility with other baselines.
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This paper uses two publicly available datasets, DBP15k and SRPRS. All source code, datasets and models in this article can be downloaded from https://github.com/Zxl001103/RREA-NoisyTriples.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China (under grant 61976077,62076085,62076087), the Natural Science Foundation of Anhui Province (under grant 2208085MF170) and the University Synergy Innovation Program of Anhui Province (GXXT-2022-040).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xiaolong Zhu, Yuhong Zhang and Xuegang Hu. The first draft of the manuscript was written by Xiaolong Zhu and the manuscript was revised by Yuhong Zhang and Xuegang Hu. All authors read and approved the final manuscript.
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Zhang, Y., Zhu, X. & Hu, X. Entity alignment in noisy knowledge graph. Appl Intell 55, 210 (2025). https://doi.org/10.1007/s10489-024-06131-4
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DOI: https://doi.org/10.1007/s10489-024-06131-4