Abstract
The prosperous development of cognition and human-level intelligence have made the creation of large-scale domain knowledge graphs (KGs) a hot research field. Entity alignment (EA), which discovers entity pairs that represent the same real object in different KGs, is the key technology for building large scale knowledge graph. Existing EA methods rely mainly on the vector representation of the entity and ignoring the semantic features of an entity in a KG, result in poor alignment quality. To address this limitation, we propose a new EA framework based on knowledge embeddings (KEs) and type matching constraints. By embedding two KGs into a unified vector space, we combine the similarity of the entity vector and the degree of the entity type matching to perform more accurate cross-KG EA. The experimental results show that our proposed method has very good performance on both cross-KG and cross-language data sets. On the DBP-YG dataset, in terms of Hit@1, Hits@10, and MRR, our methods have an average increase of 27% over the baseline method MTransE. In most cases, our method significantly improves the accuracy of EA compared with state-of-the-art methods.
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Acknowledgements
This research was funded by the National Development and Reform Commission 2018 Digital Economy Pilot Project (2018FGW005), by the Key Research Plan for State Commission of the Science and Technology of China (2018YFC0807501), and by the Foundation of Science & Technology Department of Sichuan Province, under Grant Nos. (2018HH0075, 2018JY0605, 2018JY0073, 2017KP035, 2017JZ0031).
Funding
The National Development and Reform Commission 2018 Digital Economy Pilot Project (2018FGW005) the Key Research Plan for State Commission of the Science and Technology of China (2018YFC0807501) the Foundation of Science & Technology Department of Sichuan Province, under Grant Nos. (2018HH0075, 2018JY0605, 2018JY0073, 2017KP035, 2017JZ0031).
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Lu, G., Zhang, L., Jin, M. et al. Entity alignment via knowledge embedding and type matching constraints for knowledge graph inference. J Ambient Intell Human Comput 13, 5199–5209 (2022). https://doi.org/10.1007/s12652-020-02821-2
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DOI: https://doi.org/10.1007/s12652-020-02821-2