OntoTKGE: Ontology-Enhanced Temporal Knowledge Graph Extrapolation

2026-04-07

AI Summary

This work addresses the challenge of predicting sparse entities in temporal knowledge graph extrapolation by proposing an encoder-decoder framework that integrates ontological knowledge with temporal information. The approach innovatively incorporates an ontology-view knowledge graph, enabling sparse entities to inherit behavioral patterns from their conceptual categories, thereby mitigating data sparsity. This design allows for flexible and seamless integration with a variety of existing temporal extrapolation models. Experimental results demonstrate that the proposed method significantly outperforms current baselines across four standard benchmarks, yielding substantial improvements in future fact prediction performance.

Technology Category

Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal DataKnowledge Representation and Reasoning: Geometric, Spatial, and Temporal ReasoningPlanning, Routing, and Scheduling: Temporal Planning

Application Category

Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsGraph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Temporal knowledge graph (TKG) extrapolation is an important task that aims to predict future facts through historical interaction information within KG snapshots. A key challenge for most existing TKG extrapolation models is handling entities with sparse historical interaction. The ontological knowledge is beneficial for alleviating this sparsity issue by enabling these entities to inherit behavioral patterns from other entities with the same concept, which is ignored by previous studies. In this paper, we propose a novel encoder-decoder framework OntoTKGE that leverages the ontological knowledge from the ontology-view KG (i.e., a KG modeling hierarchical relations among abstract concepts as well as the connections between concepts and entities) to guide the TKG extrapolation model's learning process through the effective integration of the ontological and temporal knowledge, thereby enhancing entity embeddings. OntoTKGE is flexible enough to adapt to many TKG extrapolation models. Extensive experiments on four data sets demonstrate that OntoTKGE not only significantly improves the performance of many TKG extrapolation models but also surpasses many SOTA baseline methods.

Problem

• Temporal Knowledge Graph
• Extrapolation
• Entity Sparsity
• Ontological Knowledge
• Future Fact Prediction

Innovation

• Temporal Knowledge Graph
• Ontology-enhanced
• Knowledge Graph Extrapolation
• Entity Embedding
• Sparse Interaction