Knowledge Graphs capture entities and their relationships. However, many knowledge graphs are afflicted by missing data. Recently, embedding methods have been used to alleviate this issue via knowledge graph completion. However, most existing methods only consider the relationship in triples, even though contextual relation types, consisting of the surrounding relation types of a triple, can substantially improve prediction accuracy. Therefore, we propose a contextual embedding method that learns the embeddings of entities and predicates while taking contextual relation types into account. The main benefits of our approach are: (1) improved scalability via a reduced number of epochs needed to achieve comparable or better results with the same memory complexity, (2) higher prediction accuracy (an average of 14%) compared to the related algorithms, and (3) high accuracy for both missing entity and predicate predictions. The source code and the YAGO43k dataset of this paper can be found from (https://github.ncsu.edu/cmoon2/kg).
Moon, Changsung; Harenberg, Steve; Slankas, John; and Samatova, Nagiza F., "Learning Contextual Embeddings for Knowledge Graph Completion" (2017). PACIS 2017 Proceedings. 248.