Enhanced Temporal Knowledge Graph Completion via Learning High-Order Connectivity and Attribute Information

Abstract

Temporal knowledge graph completion (TKGC) refers to the prediction and filling in of missing facts on time series, which is essential for many downstream applications. However, many existing TKGC methods suffer from two limitations: (1) they only consider direct relations between entities and fail to express high-order structural dependencies between entities; and (2) they only leverage relation quadruples of temporal knowledge graphs, ignoring attribute information that contains rich semantic information. This makes them vulnerable to sparsity and incompleteness problems. In response, we propose HCAE, a temporal knowledge graph completion model that includes high-order connectivity and attribute information. This consists mainly of a recursive embedding propagation layer and a multi-head attention aggregation layer. The former leverages a recursive mechanism to update entity embeddings and can learn high-order connectivity information between entities in linear complexity time. The latter leverages an attention mechanism to understand the importance of different attributes for entity representation automatically. Combining high-order connectivity and attribute information can lead to more diverse entity representations and help enhance the model’s ability to infer unknown entities. Comparative experiments on three real-world datasets show that the model’s inference accuracy significantly outperforms other benchmark methods, especially regarding knowledge graphs with many unknown entities or relations.