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TransEdge: Translating Relation-Contextualized Embeddings for Knowledge Graphs

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The Semantic Web – ISWC 2019 (ISWC 2019)

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Learning knowledge graph (KG) embeddings has received increasing attention in recent years. Most embedding models in literature interpret relations as linear or bilinear mapping functions to operate on entity embeddings. However, we find that such relation-level modeling cannot capture the diverse relational structures of KGs well. In this paper, we propose a novel edge-centric embedding model TransEdge, which contextualizes relation representations in terms of specific head-tail entity pairs. We refer to such contextualized representations of a relation as edge embeddings and interpret them as translations between entity embeddings. TransEdge achieves promising performance on different prediction tasks. Our experiments on benchmark datasets indicate that it obtains the state-of-the-art results on embedding-based entity alignment. We also show that TransEdge is complementary with conventional entity alignment methods. Moreover, it shows very competitive performance on link prediction.

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    In the following, \((\mathtt {head}, \mathtt {relation}, \mathtt {tail})\) is abbreviated as (hrt).

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This work is funded by the National Natural Science Foundation of China (No. 61872172), and the Key R&D Program of Jiangsu Science and Technology Department (No. BE2018131).

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Correspondence to Wei Hu .

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Sun, Z., Huang, J., Hu, W., Chen, M., Guo, L., Qu, Y. (2019). TransEdge: Translating Relation-Contextualized Embeddings for Knowledge Graphs. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11778. Springer, Cham.

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