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Knowledge graphs (KGs) are of great importance to many real worldapplications, but they generally suffer from incomplete information in the formof missing relations between entities. Knowledge graph completion (also knownas relation prediction) is the task of inferring missing facts given existingones. Most of the existing work is proposed by maximizing the likelihood ofobserved instance-level triples. Not much attention, however, is paid to theontological information, such as type information of entities and relations. Inthis work, we propose a type-augmented relation prediction (TaRP) method, wherewe apply both the type information and instance-level information for relationprediction. In particular, type information and instance-level information areencoded as prior probabilities and likelihoods of relations respectively, andare combined by following Bayes' rule. Our proposed TaRP method achievessignificantly better performance than state-of-the-art methods on fourbenchmark datasets: FB15K, FB15K-237, YAGO26K-906, and DB111K-174. In addition,we show that TaRP achieves significantly improved data efficiency. Moreimportantly, the type information extracted from a specific dataset cangeneralize well to other datasets through the proposed TaRP model.

Type-augmented Relation Prediction in Knowledge Graphs