Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

ACL 2019 Deepak NathaniJatin ChauhanCharu SharmaManohar Kaul

The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction). Several recent works suggest that convolutional neural network (CNN) based models generate richer and more expressive feature embeddings and hence also perform well on relation prediction... (read more)

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Evaluation results from the paper

Task Dataset Model Metric name Metric value Global rank Compare
Link Prediction FB15k-237 KBAT MRR 0.518 # 1
Link Prediction FB15k-237 KBAT [email protected] 0.626 # 1
Link Prediction FB15k-237 KBAT [email protected] 0.54 # 1
Link Prediction FB15k-237 KBAT [email protected] 0.46 # 1
Knowledge Graph Embeddings FB15k-237 KBAT [email protected] 62.6 # 1