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A knowledge graph is a powerful representation of real-world entities andtheir relations. The vast majority of these relations are defined as positivestatements, but the importance of negative statements is increasinglyrecognized, especially under an Open World Assumption. Explicitly consideringnegative statements has been shown to improve performance on tasks such asentity summarization and question answering or domain-specific tasks such asprotein function prediction. However, no attention has been given to theexploration of negative statements by knowledge graph embedding approachesdespite the potential of negative statements to produce more accuraterepresentations of entities in a knowledge graph.  We propose a novel approach, TrueWalks, to incorporate negative statementsinto the knowledge graph representation learning process. In particular, wepresent a novel walk-generation method that is able to not only differentiatebetween positive and negative statements but also take into account thesemantic implications of negation in ontology-rich knowledge graphs. This is ofparticular importance for applications in the biomedical domain, where theinadequacy of embedding approaches regarding negative statements at theontology level has been identified as a crucial limitation.  We evaluate TrueWalks in ontology-rich biomedical knowledge graphs in twodifferent predictive tasks based on KG embeddings: protein-protein interactionprediction and gene-disease association prediction. We conduct an extensiveanalysis over established benchmarks and demonstrate that our method is able toimprove the performance of knowledge graph embeddings on all tasks.

Biomedical Knowledge Graph Embeddings with Negative Statements