# Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

## TL;DR

This paper introduces an attention-based embedding model for knowledge graphs that captures local neighborhood information, relation clusters, and multi-hop relations, leading to improved relation prediction accuracy.

## Contribution

It proposes a novel attention mechanism for embeddings that incorporates neighborhood, relation clusters, and multi-hop relations, enhancing knowledge graph completion.

## Key findings

- Significant performance improvements over state-of-the-art methods.
- Effective modeling of local neighborhood and multi-hop relations.
- Empirical validation on multiple datasets.

## Abstract

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. However, we observe that these KG embeddings treat triples independently and thus fail to cover the complex and hidden information that is inherently implicit in the local neighborhood surrounding a triple. To this effect, our paper proposes a novel attention based feature embedding that captures both entity and relation features in any given entity's neighborhood. Additionally, we also encapsulate relation clusters and multihop relations in our model. Our empirical study offers insights into the efficacy of our attention based model and we show marked performance gains in comparison to state of the art methods on all datasets.