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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Zijun Cui
- sl:arxiv_num : 2009.07938
- sl:arxiv_published : 2020-09-16T21:14:18Z
- sl:arxiv_summary : Knowledge graphs (KGs) are of great importance to many real world
applications, but they generally suffer from incomplete information in the form
of missing relations between entities. Knowledge graph completion (also known
as relation prediction) is the task of inferring missing facts given existing
ones. Most of the existing work is proposed by maximizing the likelihood of
observed instance-level triples. Not much attention, however, is paid to the
ontological information, such as type information of entities and relations. In
this work, we propose a type-augmented relation prediction (TaRP) method, where
we apply both the type information and instance-level information for relation
prediction. In particular, type information and instance-level information are
encoded as prior probabilities and likelihoods of relations respectively, and
are combined by following Bayes' rule. Our proposed TaRP method achieves
significantly better performance than state-of-the-art methods on three
benchmark datasets: FB15K, YAGO26K-906, and DB111K-174. In addition, we show
that TaRP achieves significantly improved data efficiency. More importantly,
the type information extracted from a specific dataset can generalize well to
other datasets through the proposed TaRP model.@en
- sl:arxiv_title : Type-augmented Relation Prediction in Knowledge Graphs@en
- sl:arxiv_updated : 2020-09-16T21:14:18Z
- sl:bookmarkOf : https://arxiv.org/abs/2009.07938
- sl:creationDate : 2020-09-19
- sl:creationTime : 2020-09-19T10:00:31Z
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