Beyond Homogeneous Rules: Ontology-Aware Logical Reasoning for Temporal Knowledge Graphs

Extrapolative temporal knowledge graph reasoning (TKGR) requires models to predict facts that have not yet appeared, using history before the query time. Most existing methods organize temporal regularities by relation: rule-based methods index rules by relation and share them among all entities, and generative methods retrieve evidence by relation. Entities barely participate in the parameterization of the regularities. However, our experiments show that the relation-centric temporal rule base cannot be reused at equal accuracy across different semantic types, leading to coverage failure and ranking failure. Meanwhile, what generative reasoning truly relies on is the temporal structure executable on the graph rather than name semantics, and large language models (LLMs) can barely answer correctly when the answer never enters the evidence. In this paper, we propose OnLoR, an Ontology-aware Logical Reasoning method, which applies the established ontology concept from knowledge representation to temporal rules: through semantic-based ontology induction and the ontology-aware rule learning module OnRule, the same temporal path obtains different confidence values on different ontology classes, while generic rules not bound to any ontology are retained to maintain coverage. We further change the role of the LLM, which no longer generates answers from names, but performs constrained pairwise correction on adjacent candidates over the logical evidence packs provided by OnRule. Comprehensive experiments on four TKGR datasets show that OnLoR improves over both the rule-based and generative methods, achieving state-of-the-art in most settings.