Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul

[How to correct problems with metadata yourself]

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 multi-hop 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.
Anthology ID:
P19-1466
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4710–4723
Language:
URL:
https://aclanthology.org/P19-1466
DOI:
10.18653/v1/P19-1466
Bibkey:
Cite (ACL):
Deepak Nathani, Jatin Chauhan, Charu Sharma, and Manohar Kaul. 2019. Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4710–4723, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (Nathani et al., ACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/P19-1466.pdf
Code
 deepakn97/relationPrediction +  additional community code
Data
FB15k, FB15k-237, NELL-995, WN18, WN18RR
PDF Search Code