Mining Latent Features of Knowledge Graphs for Predicting Missing Relations

Knowledge Graphs (KGs) model statements as head-relation-tail triples. Intrinsically, KGs are assumed incomplete especially when knowledge is represented under the Open World Assumption. The problem of KG completeness aims at identifying missing values. While some approaches focus on predicting relations between pairs of known nodes in a graph, other solutions have studied the problem of predicting missing entity properties or relations even in the presence of unknown tails. In this work, we address the latter research problem: for a given head entity in a KG, obtain the set of relations which are missing for the entity. To tackle this problem, we present an approach that mines latent information about head entities and their relations in KGs. Our solution combines in a novel way, state-of-the-art techniques from association rule learning and community detection to discover latent groups of relations in KGs. These latent groups are used for predicting missing relations of head entities in a KG. Our results on ten KGs show that our approach is complementary state-of-the-art solutions.