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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Deepak Nathani
- sl:arxiv_num : 1906.01195
- sl:arxiv_published : 2019-06-04T04:59:08Z
- sl:arxiv_summary : 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.@en
- sl:arxiv_title : Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs@en
- sl:arxiv_updated : 2019-06-04T04:59:08Z
- sl:bookmarkOf : https://arxiv.org/abs/1906.01195
- sl:creationDate : 2020-04-30
- sl:creationTime : 2020-04-30T12:59:24Z
- sl:relatedDoc : http://www.semanlink.net/doc/2020/04/deepak_nathani_%7C_pay_attention_
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