{"id":"W4414925507","doi":"10.1016/j.knosys.2025.114537","title":"Efficient cluster-guided key timestamp discovery for temporal knowledge graph completion","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Hubei University; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Timestamp; Cluster analysis; Key (lock); Snapshot (computer storage); Relation (database); Temporal database; Graph; Feature learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007964251,0.001017796,0.001749463,0.002465735,0.001181081,0.001683778,0.002812801,0.001092409,0.004494321],"category_scores_gemma":[0.006235545,0.0005298586,0.0009737094,0.003762451,0.0007172184,0.003053555,0.002962132,0.00170786,0.001630848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001379129,"about_ca_system_score_gemma":0.00472886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02139063,"about_ca_topic_score_gemma":0.03404622,"domain_scores_codex":[0.9987624,0.0001420407,0.0000847733,0.000416374,0.0004069833,0.0001874516],"domain_scores_gemma":[0.9975387,0.0007604061,0.0002157346,0.0006839861,0.000625669,0.0001756526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001003404,0.0003702613,0.002575975,0.0004806838,0.0002105627,0.0002985433,0.0003921999,0.2025361,0.02006142,0.04100843,0.03104864,0.7000138],"study_design_scores_gemma":[0.00002622079,0.00004296641,0.0003824827,0.00001323238,0.00003576906,0.00008126979,0.0001126617,0.9583936,0.006266235,0.03172635,0.002898061,0.00002118879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01841617,0.00047494,0.9746915,0.000309044,0.0001497801,0.0001644673,0.00108854,0.003396042,0.001309565],"genre_scores_gemma":[0.453393,0.0004623001,0.535665,0.0001820077,0.0001078464,0.000166471,0.004899634,0.0003930962,0.004730671],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02139063,"threshold_uncertainty_score":0.04253221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02323406851963412,"score_gpt":0.292981318411024,"score_spread":0.2697472498913899,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}