{"id":"W4386566692","doi":"10.18653/v1/2023.eacl-main.150","title":"TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Google (Canada)","funders":"Science and Engineering Research Board","keywords":"Computer science; Knowledge graph; Question answering; Convolution (computer science); Graph; Artificial intelligence; Theoretical computer science; Artificial neural network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005982199,0.0001662155,0.0001707123,0.0003654794,0.0001732012,0.0001205724,0.0004831554,0.00009974041,0.00001184881],"category_scores_gemma":[0.00002779785,0.0001562655,0.0001235585,0.0009226679,0.00003057572,0.0006438781,0.0001660227,0.00009552709,0.00006766716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004820871,"about_ca_system_score_gemma":0.0000783918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001546677,"about_ca_topic_score_gemma":0.0001094542,"domain_scores_codex":[0.9985405,0.00005422039,0.000319769,0.0005088823,0.0001996817,0.0003768986],"domain_scores_gemma":[0.9991413,0.00008209252,0.0000804427,0.0004573416,0.0001382357,0.0001005461],"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":[0.00002255756,0.00007459819,0.0100431,0.0001010222,0.00003739348,0.0000088327,0.0007807727,0.00037205,0.003301358,0.9543384,0.0104319,0.02048797],"study_design_scores_gemma":[0.0006227331,0.00007751489,0.007019155,0.00004373432,0.000006529021,0.00000379791,0.00002871934,0.8990211,0.0009181497,0.08520856,0.00675888,0.0002910913],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1100512,0.000138215,0.8853212,0.0004995297,0.001039513,0.0003891609,0.000002927664,0.001370518,0.001187703],"genre_scores_gemma":[0.9233389,0.00002259728,0.07460386,0.0001147193,0.0001192069,0.00007832267,0.00002998941,0.00001839806,0.001673935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8986491,"threshold_uncertainty_score":0.6372321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02940984131595029,"score_gpt":0.2913399630388992,"score_spread":0.2619301217229489,"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."}}