Abstract
Considering the diversity and heterogeneity of different knowledge graphs, it is necessary to logically establish a comprehensive, accurate and unified knowledge repository. We design a framework by importing active learning strategies to neural network models for entity alignment, aiming to create informative seeds for more efficient entity alignment models with lower annotation cost. The model measures the benefit of an entity being selected from the two aspects of its uncertainty and influence. Extensive experiments are conducted on two benchmark datasets, and the results show that our method achieves significant improvement over the existing models.
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Acknowledgement
This work was supported by National Natural Science Foundation of China (Grant No. 61902074) and Science and Technology Committee Shanghai Municipality (Grant No. 19ZR1404900).
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Pan, J., Zheng, W. (2023). Knowledge Graph Entity Alignment Powered by Active Learning. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13422. Springer, Cham. https://doi.org/10.1007/978-3-031-25198-6_24
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DOI: https://doi.org/10.1007/978-3-031-25198-6_24
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