M[formula omitted]ixKG: Mixing for harder negative samples in knowledge graph

Abstract

Knowledge graph embedding (KGE) involves mapping entities and relations to low-dimensional dense embeddings, enabling a wide range of real-world applications. The mapping is achieved via distinguishing the positive and negative triplets in knowledge graphs. Therefore, how to design high-quality negative triplets is critical in the effectiveness of KEG models. Existing KGE models face challenges in generating high-quality negative triplets. Some models employ simple static distributions, i.e. uniform or Bernoulli distribution, and it is difficult for these methods to be trained distinguishably because of the sampled uninformative negative triplets. Furthermore, current methods are confined to constructing negative triplets from existing entities within the knowledge graph, limiting their ability to explore harder negatives. We introduce a novel mixing strategy in knowledge graphs called M2ixKG. M2ixKG adopts mixing operation in generating harder negative samples from two aspects: one is mixing among the heads and tails in triplets with the same relation to strengthen the robustness and generalization of the entity embeddings; the other is mixing the negatives with high scores to generate harder negatives. Our experiments, utilizing three datasets and four classical score functions, highlight the exceptional performance of M2ixKG in comparison to previous negative sampling algorithms.