Conference Proceedings

Entity Alignment between Knowledge Graphs Using Attribute Embeddings

Bayu Distiawan Trisedya, Jianzhong Qi, Rui Zhang

Proceedings of the AAAI Conference on Artificial Intelligence | Association for the Advancement of Artificial Intelligence Press | Published : 2019


The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based models are proposed for this task. Such models are built on top of a knowledge graph embedding model that learns entity embeddings to capture the semantic similarity between entities in the same knowledge graph. We propose to learn embeddings that can capture the similarity between entities in different knowledge graphs. Our proposed model helps align entities from different knowledge graphs, and hence enables the integration of multiple knowledge graphs. Our model exploits large numbers of attribute triples existing in the k..

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Awarded by Australian Research Council (ARC)

Awarded by National Science Foundation of China

Funding Acknowledgements

Bayu Distiawan Trisedya is supported by the Indonesian Endowment Fund for Education (LPDP). This work is supported by Australian Research Council (ARC) Discovery Project DP180102050, Google Faculty Research Award, and the National Science Foundation of China (Project No. 61872070 and No. 61402155).