Knowledge Graphs (KGs) currently contain a vast amount of structured information in the form of entities and relations.
Because KGs are often constructed automatically by means of information extraction processes, they may miss information
that was either not present in the original source or not successfully extracted. As a result, KGs might potentially lack useful
and valuable information. Current approaches that aim to complete missing information in KGs either have a dependence on
embedded representations, which hinders their scalability and applicability to different KGs; or are based on long random paths
that may not cover relevant information by mere chance, since exhaustively analyzing all possible paths of a large length between
entities is very time-consuming. In this paper, we present an approach to completing KGs based on evaluating candidate triples
using a novel set of features, which exploits the highly relational nature of KGs by analyzing the entities and relations surrounding
any given pair of entities. Our results show that our proposal is able to identify correct triples with a higher effectiveness than
other state-of-the-art approaches (up to 60% higher precision or 20% higher recall in some datasets).Ministerio de Economía y Competitividad TIN2016-75394-
