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This paper introduces OntoTKGE, a novel encoder-decoder framework that leverages ontological knowledge to improve temporal knowledge graph (TKG) extrapolation, particularly for entities with sparse historical interactions. OntoTKGE integrates an ontology-view KG, modeling hierarchical relations among concepts and connections between concepts and entities, to guide the TKG extrapolation model's learning process. Experiments on four datasets show that OntoTKGE significantly improves the performance of existing TKG extrapolation models and outperforms state-of-the-art baselines.
Injecting ontological knowledge into temporal knowledge graph extrapolation models can substantially boost performance, especially for sparsely connected entities.
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.
