RoAN: A relation-oriented attention network for temporal knowledge graph completion

In the last few years, the availability of temporal knowledge graphs (TKGs), which associate time information for each event, increased the need for completing in these TKGs. In order to solve the completion problem, the existing efforts extend static knowledge graph completion models to handle time-dependent representations, or encode the structural information of events for passing temporal message. However, the above efforts mostly construct TKGs in the form of multi-edges mesh and focus on entity features. They do not explore the power of relations for TKG completion task. In this paper, we introduce a form of relational multi-chains to reconstruct TKGs and propose a relation-oriented attention mechanism for embedding the features of relations. According to our attention mechanism, we build a Relation-oriented Attention Network (RoAN) to model temporal embeddings of relations. It is worth noting that our approach is model-agnostic and can be potentially combined with most existing TKG completion models. Experimental results show that our approach can be coupled with previous TKGC methods and can increase their performance accordingly. In addition, the analysis reveals the principle of our relation-oriented attention mechanism.