Understanding and Improving Knowledge Graph Embedding for Entity Alignment

Lingbing Guo, Qiang Zhang, Zequn Sun, Mingyang Chen, Wei Hu, Huajun Chen
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:8145-8156, 2022.

Abstract

Embedding-based entity alignment (EEA) has recently received great attention. Despite significant performance improvement, few efforts have been paid to facilitate understanding of EEA methods. Most existing studies rest on the assumption that a small number of pre-aligned entities can serve as anchors connecting the embedding spaces of two KGs. Nevertheless, no one has investigated the rationality of such an assumption. To fill the research gap, we define a typical paradigm abstracted from existing EEA methods and analyze how the embedding discrepancy between two potentially aligned entities is implicitly bounded by a predefined margin in the score function. Further, we find that such a bound cannot guarantee to be tight enough for alignment learning. We mitigate this problem by proposing a new approach, named NeoEA, to explicitly learn KG-invariant and principled entity embeddings. In this sense, an EEA model not only pursues the closeness of aligned entities based on geometric distance, but also aligns the neural ontologies of two KGs by eliminating the discrepancy in embedding distribution and underlying ontology knowledge. Our experiments demonstrate consistent and significant performance improvement against the best-performing EEA methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-guo22i, title = {Understanding and Improving Knowledge Graph Embedding for Entity Alignment}, author = {Guo, Lingbing and Zhang, Qiang and Sun, Zequn and Chen, Mingyang and Hu, Wei and Chen, Huajun}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {8145--8156}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/guo22i/guo22i.pdf}, url = {https://proceedings.mlr.press/v162/guo22i.html}, abstract = {Embedding-based entity alignment (EEA) has recently received great attention. Despite significant performance improvement, few efforts have been paid to facilitate understanding of EEA methods. Most existing studies rest on the assumption that a small number of pre-aligned entities can serve as anchors connecting the embedding spaces of two KGs. Nevertheless, no one has investigated the rationality of such an assumption. To fill the research gap, we define a typical paradigm abstracted from existing EEA methods and analyze how the embedding discrepancy between two potentially aligned entities is implicitly bounded by a predefined margin in the score function. Further, we find that such a bound cannot guarantee to be tight enough for alignment learning. We mitigate this problem by proposing a new approach, named NeoEA, to explicitly learn KG-invariant and principled entity embeddings. In this sense, an EEA model not only pursues the closeness of aligned entities based on geometric distance, but also aligns the neural ontologies of two KGs by eliminating the discrepancy in embedding distribution and underlying ontology knowledge. Our experiments demonstrate consistent and significant performance improvement against the best-performing EEA methods.} }
Endnote
%0 Conference Paper %T Understanding and Improving Knowledge Graph Embedding for Entity Alignment %A Lingbing Guo %A Qiang Zhang %A Zequn Sun %A Mingyang Chen %A Wei Hu %A Huajun Chen %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-guo22i %I PMLR %P 8145--8156 %U https://proceedings.mlr.press/v162/guo22i.html %V 162 %X Embedding-based entity alignment (EEA) has recently received great attention. Despite significant performance improvement, few efforts have been paid to facilitate understanding of EEA methods. Most existing studies rest on the assumption that a small number of pre-aligned entities can serve as anchors connecting the embedding spaces of two KGs. Nevertheless, no one has investigated the rationality of such an assumption. To fill the research gap, we define a typical paradigm abstracted from existing EEA methods and analyze how the embedding discrepancy between two potentially aligned entities is implicitly bounded by a predefined margin in the score function. Further, we find that such a bound cannot guarantee to be tight enough for alignment learning. We mitigate this problem by proposing a new approach, named NeoEA, to explicitly learn KG-invariant and principled entity embeddings. In this sense, an EEA model not only pursues the closeness of aligned entities based on geometric distance, but also aligns the neural ontologies of two KGs by eliminating the discrepancy in embedding distribution and underlying ontology knowledge. Our experiments demonstrate consistent and significant performance improvement against the best-performing EEA methods.
APA
Guo, L., Zhang, Q., Sun, Z., Chen, M., Hu, W. & Chen, H.. (2022). Understanding and Improving Knowledge Graph Embedding for Entity Alignment. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:8145-8156 Available from https://proceedings.mlr.press/v162/guo22i.html.

Related Material