Improving Embeddings by Refining Meanings for Temporal Knowledge Graph Link Predictions

Abstract

Temporal Knowledge Graphs (TKGs) represent real-life facts using entities, relational types, and timestamps where relational types state the semantic scenario of facts. Current methods learn embeddings by merging facts of multiple types (e.g. sport and family) for predictions. Such embeddings associate well with relations of multiple types. However, the prediction needs only information of a single type, i.e. embeddings contain irrelevant relational types. In this paper, we explore whether embeddings with irrelevant information confuse predictions and propose RefE to improve embeddings by refining meanings. RefE enhances the ability for predicting links of a specific type while maintaining associations of multiple relational types. RefE consists of general learning and embedding refining modules. General learning embeds facts of multiple types to represent general meanings, and embedding refining emphasizes facts of the single type that matches the prediction. RefE uses both general and refined embeddings for predictions. Experimental results on four datasets verify the effectiveness of RefE.