Knowledge graph embedding has been proposed to embed entities and relations into continuous vector spaces, which can benefit various downstream tasks, such as question answering and recommender systems, etc. A common assumption of existing knowledge graph embedding models is that the relation is a translation vector connecting the embedded head entity and tail entity. However, based on this assumption, the same relation connecting multiple entities may form a circle and lead to mistakes during computing process. To solve this so-called circular relation problem that has been ignored previously, we propose a novel method called ContE ( Cont extualized E mbedding) for knowledge graphs by exploring collaborative relations. Specifically, each collaborative relation combines an explicit relation and a latent relation, where the explicit one is the original relation between two entities, and the latent one is introduced to capture the implicit interactions obtained via the context information of the two entities. With the collaborative relations, the same relations will be embedded varying across different contexts, and the sharing of similar semantics will be guaranteed as well. We conduct extensive experiments in link prediction and triple classification tasks on five benchmark datasets. The experimental results demonstrate that the proposed ContE achieves improvements over several baselines.
原文链接
分享
引用
