- author: Zequn Sun, Jiacheng Huang, Wei Hu, Muhao Chen, Lingbing Guo and Yuzhong Qu.
- abstract: 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 headtail 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.
- keywords: Knowledge graphs · Contextualized embeddings · Entity alignment · Link prediction
- interpretation:
- pdf: paper
- code: code
- dataset: DBP15K，DWY100K，FB15k-237，WN18RR
- ppt/video:
- curator: Mengya Ji