An enhanced framework for knowledge graph embedding based on negative sample analogical reasoning

Abstract

Knowledge graph embedding (KGE) is an effective method for link prediction in knowledge graphs, with numerous models demonstrating significant success. However, many existing KGE models struggle to effectively represent complex relationships and often face challenges related to a substantial number of missing triples. While enhancing KGE models through analogical reasoning offers a potential solution, current approaches typically depend on positive samples that are similar to the target for learning analogy embeddings. In real-world applications, data imbalance and incompleteness hinder the identification of sufficient positive examples, resulting in suboptimal performance in analogical inference. To this end, we propose a novel enhanced framework called Ne_AnKGE, which is based on negative analogical reasoning. First, Ne_AnKGE uses negative sample analogical reasoning to mitigate the scarcity of similar positive samples in analogical reasoning, while enhancing the link prediction ability of the basic model. Additionally, to enhance the model's ability to represent complex relationships and overcome the limitations of relying on a single KGE model, the Ne_AnKGE framework integrates the outputs of TransE and RotatE, both of which have been enhanced through negative sample analogical reasoning. Extensive experiments on the FB15K-237 and WN18RR datasets demonstrate the competitive performance of Ne_AnKGE.