{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002375825,0.002018408,0.001476749,0.002379708,0.00090467,0.001707915,0.004098,0.002311423,0.00801715],"category_scores_gemma":[0.01134194,0.0007747307,0.001565428,0.002788429,0.0008599705,0.005361254,0.002897804,0.004058842,0.002865947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001575776,"about_ca_system_score_gemma":0.002298724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01183699,"about_ca_topic_score_gemma":0.02040484,"domain_scores_codex":[0.9983645,0.0004340262,0.0000970015,0.0005964113,0.0003845502,0.0001235636],"domain_scores_gemma":[0.9962948,0.001911419,0.0002953667,0.0009600841,0.000379029,0.0001593192],"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":[0.0004178226,0.0003542243,0.001947764,0.0006524947,0.0002732898,0.0002753559,0.0003705829,0.2788026,0.004241746,0.03972728,0.0460967,0.6268402],"study_design_scores_gemma":[0.00002979576,0.00005172267,0.0002731053,0.00002729435,0.0000315444,0.00006515886,0.00005142368,0.9362829,0.002274742,0.05488674,0.006006951,0.00001861817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006517979,0.0005246299,0.9793264,0.0005076299,0.0001419754,0.0001494239,0.002005589,0.009632609,0.001193835],"genre_scores_gemma":[0.2257227,0.0007719008,0.7516589,0.0005774299,0.0002566708,0.0005596133,0.01326316,0.0009981383,0.006191573],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01183699,"threshold_uncertainty_score":0.02682006,"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."}}