Neo-TKGC: Enhancing Temporal Knowledge Graph Completion with Integrated Node Weights and Future Information

Temporal Knowledge Graph Completion (TKGC) involves predicting and filling in missing facts within time series data, a crucial task with wide-ranging applications across various domains. The dynamic evolution of Temporal Knowledge Graphs (TKGs) adds complexity to this task, making it inherently challenging. Existing research predominantly relies on historical data to complete the missing facts. However, these approaches often overlook the potential of future information and the significance of node weights.To address these challenges, we propose Neo-TKGC, a novel temporal knowledge graph completion model that integrates a graph structure encoding module and a temporal encoding module. The graph structure encoding module introduces node weights to enhance the capabilities of graph neural networks (GNNs) for entity and relation representation learning, implemented using CompGCN. This module can be easily extended to any GNN models utilizing node and edge aggregation. The temporal encoding module leverages both future and historical information to capture relevant contexts and temporal dependencies among entities and relations.By combining node weights and future information, Neo-TKGC achieves more accurate entity and relation representations, thereby improving the model's ability to infer unknown entities. Extensive experiments on three real-world TKGC datasets demonstrate the superior performance of our model compared to existing approaches, achieving at least a 1.7% relative improvement in Hits@1 across most metrics.