Learning Attention-based Embeddings for Relation Prediction in
\nKnowledge Graphs

The recent proliferation of knowledge graphs
\n(KGs) coupled with incomplete or partial information, in the form of missing relations
\n(links) between entities, has fueled a lot of
\nresearch on knowledge base completion (also
\nknown as relation prediction). Several recent works suggest that convolutional neural
\nnetwork (CNN) based models generate richer
\nand more expressive feature embeddings and
\nhence also perform well on relation prediction.
\nHowever, we observe that these KG embeddings treat triples independently and thus fail
\nto cover the complex and hidden information
\nthat is inherently implicit in the local neighborhood surrounding a triple. To this…