{"id":"W3099845049","doi":"10.18653/v1/2020.emnlp-main.462","title":"TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":139,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Samsung; Compute Canada; Canadian Institute for Advanced Research","keywords":"Leverage (statistics); Computer science; Knowledge graph; Message passing; Graph; Temporal database; Machine learning; Artificial intelligence; Theoretical computer science; Data mining; Distributed computing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001745065,0.0002428449,0.0002995332,0.00009159333,0.0002757325,0.0001753278,0.0008264858,0.00008376721,0.00001598015],"category_scores_gemma":[0.00003742893,0.00021452,0.0001888279,0.0008593232,0.00007844214,0.0007867138,0.0002735007,0.0001890857,0.00003529605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002549329,"about_ca_system_score_gemma":0.00004235146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001002776,"about_ca_topic_score_gemma":0.00002767255,"domain_scores_codex":[0.9982554,0.00007415263,0.0003652531,0.0006511991,0.0002103522,0.0004436611],"domain_scores_gemma":[0.9989406,0.0001630355,0.0001393438,0.0003888426,0.0001157314,0.0002523935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002371105,0.0005362382,0.02633952,0.0004192477,0.0001604479,0.00009069603,0.00319468,0.002839762,0.01269898,0.5665865,0.2249403,0.1619565],"study_design_scores_gemma":[0.002303487,0.0007412569,0.002982217,0.00007250892,0.00002254856,0.00002067401,0.0001195692,0.770487,0.003974853,0.04271487,0.1753782,0.001182875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002394414,0.0002002267,0.9882629,0.005413607,0.0004003498,0.0004822804,0.000005539085,0.0007456642,0.002095007],"genre_scores_gemma":[0.8022373,0.000004144016,0.1956631,0.001606114,0.0002305871,0.00003978235,0.00002223808,0.00002230307,0.0001744413],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7998428,"threshold_uncertainty_score":0.8747872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05205755584159759,"score_gpt":0.2873606080580615,"score_spread":0.2353030522164639,"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."}}