Multi-Granularity History and Entity Similarity Learning for Temporal Knowledge Graph Reasoning

Anonymous

Abstract: Temporal Knowledge Graph (TKG) reasoning, aiming to predict future unknown facts based on historical information, has attracted considerable attention due to its great practical value. Insight into history is the key to predict the future. However, most existing TKG reasoning models singly capture repetitive history, ignoring the entity's multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning. In this paper, we propose \textbf{M}ulti-\textbf{G}ranularity History and \textbf{E}ntity \textbf{S}imilarity \textbf{L}earning (MGESL) model for Temporal Knowledge Graph Reasoning, which models historical information from both coarse-grained and fine-grained history. Since similar entities tend to exhibit similar behavioural patterns, we also design a hypergraph convolution aggregator to capture the similarity between entities. Furthermore, we introduce a more realistic setting for the TKG reasoning, where candidate entities are already known at the timestamp to be predicted. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed model.

Paper Type: long

Research Area: Information Retrieval and Text Mining

Languages Studied: English