Nathani, Deepak and Chauhan, Jatin and Sharma, Charu and Kaul, Manohar
(2019)
Learning Attention-based Embeddings for Relation Prediction in
Knowledge Graphs.
arXiv.
pp. 1-10.
(Submitted)
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.
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