Bilinear Model for Knowledge Graph Completion with Hard Negative Examples Mining

Sparsity is a challenge for knowledge graph completion. The negative sample space, which is much larger than the positive sample space, has many negative samples similar to the positive samples or potential positive samples, which affects the performance of the knowledge graph completion model. We propose a novel knowledge graph completion method, hard negative examples mining bilinear model (HNEMB). This model uses two components to model entities and relations, namely models relations into relation matrices and inverse relation matrices to learn latent inverse semantics of relations, and models entities into head vectors and tail vectors to learn different representations of entity in different positions. Then we calculate the bilinear scores of triples, we choose correct candidates based on the scores. To address the problem of sparsity of the knowledge graph, we first propose a method for mining hard negative examples for knowledge graph completion, to make model effectively capture the associations between head entities, relations and tail entities. Finally, experiments on multiple knowledge graph completion datasets shows that, as compare with baselines, our model achieves significant improvements and shows its advantages to model sparse knowledge graph.