In this article, we propose a novel approach called LMS (Leveraging Multi-Modal Knowledge Graphs for Temporal Relation Extrapolation) to address the challenge of predicting future events in temporal knowledge graphs (TKGs). TKGs are rich in semantics, but their complexity can lead to computational overhead and difficulties in capturing entity characteristics across different timestamps. To overcome these limitations, LMS leverages multi-modal information within TKGs, including graph structure, timestamp sequences, and extrinsic features.
The proposed approach consists of five key components
- Evolutional Graph Learning: This component focuses on capturing correlations among concurrent facts and modeling temporal evolution of snapshots. By learning the underlying patterns in the graph structure, LMS can better understand the relationships between entities and predict future events.
- Union Graph Learning: In this component, a query-specific union graph is constructed based on the snapshots, and the structural information within the graph is learned. This allows LMS to capture complex patterns in the data and make more accurate predictions.
- Temporal Graph Learning: This component constructs an auxiliary graph that incorporates timestamps and models the periodic interaction between them. By taking into account the temporal relationships between entities, LMS can better understand how they are connected and predict future events.
- Indicator: The indicator component is responsible for gathering statistics related to historical facts and using them to refine predictions. By leveraging the statistical properties of the data, LMS can improve its accuracy and reduce overfitting.
- Time-aware Decoder: Finally, the time-aware decoder integrates temporal information in ConvTransE to predict future facts. By combining the strengths of ConvTransE with temporal information, LMS can make more accurate predictions about future events.
Overall, LMS offers a powerful approach to predicting future events in TKGs by leveraging their multi-modal information. By capturing complex patterns in the data and using statistical techniques to refine predictions, LMS demonstrates significant improvements in accuracy compared to existing methods. With its ability to handle large and complex datasets, LMS has the potential to be applied in a wide range of domains, from social network analysis to recommendation systems.
