Improving knowledge graph completion via increasing embedding interactions

Knowledge graphs usually consist of billions of triplet facts describing the real world. Although most of the existing knowledge graphs are huge in scale, they are still far from completion. As a result, varieties of knowledge graph embedding approaches have emerged, which have been proven to be an effective and efficient solution for knowledge graph completion. In this paper, we devise a novel knowledge graph embedding model named InterERP, which aims to improve model performance by increasing Inter actions between the embeddings of E ntities, R elations and relation P aths. Specifically, we first introduce the interaction matrix to obtain the interaction embeddings of entities and relations. Then, we employ the Inception network to learn the query embedding, which can further increase the interactions between entities and relations. Furthermore, we resort to logical rules to construct semantic relation paths and are committed to modeling the interactions between different relations in a relation path. The experimental results on four commonly used datasets, demonstrate that the proposed InterERP matches or outperforms the state-of-the-art approaches.