{"type":"ScholarlyArticle","title":"Strikingness-Aware Evaluation for Temporal Knowledge Graph Reasoning","authors":[{"name":"Rikui Huang","slug":"rikui-huang","given":"Rikui","family":"Huang","affiliation":""},{"name":"Shengzhe Zhang","slug":"shengzhe-zhang","given":"Shengzhe","family":"Zhang","affiliation":""},{"name":"Wei Wei","slug":"wei-wei","given":"Wei","family":"Wei","affiliation":""}],"abstract":"Temporal Knowledge Graph Reasoning (TKGR) aims at inferring missing (especially future) events from historical data. Current evaluation in TKGR uniformly weights all events, ignoring that most are trivial repetitions, which overestimate the true reasoning ability. Therefore, the rare outstanding events, whose prediction demands deeper reasoning, should be distinguished and emphasized. To this end, we propose a strikingness-aware evaluation framework, which introduces a rule-based strikingness measuring framework (RSMF) to quantify event strikingness by comparing its expected occurrence with peer events derived from temporal rules. Strikingness is then integrated as a weighting factor into metrics like weighted MRR and Hits@k. Experiments on four TKG benchmarks reveal: 1) All representative models perform worse as event strikingness increases, 2) Path-based methods excel on low-strikingness events and representation-based ones on high-strikingness events, 3) We design an ensemble method whose gains stem from fitting trivial events rather than reasoning improvement.  Our framework provides a more rigorous evaluation, refocusing the field on predicting outstanding events.","doi":"10.24963/ijcai.2026/432","url":"https://ijcai.org/proceedings/2026/432","pdf":"https://ijcai.org/proceedings/2026/0432.pdf","markdown":"https://ijcai.org/proceedings/2026/432.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/432","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":432,"pages":{"first":3881,"last":3889},"keywords":["Knowledge Representation and Reasoning: Preference modelling and preference-based reasoning","Knowledge Representation and Reasoning: Qualitative, geometric, spatial, and temporal reasoning","Knowledge Representation and Reasoning: Semantic Web","Knowledge Representation and Reasoning: Learning and reasoning","Knowledge Representation and Reasoning: Applications"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}