{"id":"W4324355422","doi":"10.3390/data8030061","title":"TKGQA Dataset: Using Question Answering to Guide and Validate the Evolution of Temporal Knowledge Graph","year":2023,"lang":"en","type":"article","venue":"Data","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Bank of Canada","funders":"","keywords":"Knowledge graph; Computer science; Question answering; Graph; Parsing; Information retrieval; Process (computing); Artificial intelligence; Natural language processing; Theoretical computer science","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":[0.00163265,0.002008792,0.0007875399,0.005549136,0.001331805,0.001826596,0.00243051,0.0029469,0.006047126],"category_scores_gemma":[0.01019936,0.0003817902,0.001585342,0.004287284,0.0006371806,0.003424722,0.001618838,0.001896805,0.007039062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002205411,"about_ca_system_score_gemma":0.001922859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05439519,"about_ca_topic_score_gemma":0.0856514,"domain_scores_codex":[0.9979534,0.0004286067,0.00032076,0.0006672306,0.0004852306,0.0001447404],"domain_scores_gemma":[0.9947412,0.001836651,0.0005363427,0.00116533,0.001361989,0.0003583642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008109131,0.0007023546,0.01778341,0.003728882,0.0003899072,0.0008088142,0.001001113,0.008120636,0.008575771,0.004500906,0.8671174,0.08645992],"study_design_scores_gemma":[0.0005661293,0.0003996951,0.04934801,0.0006039458,0.0003522227,0.001150955,0.002123306,0.0876628,0.01375981,0.01055283,0.8332495,0.0002308246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0334544,0.002073281,0.009118727,0.001305321,0.0003325365,0.0004674081,0.9327068,0.01406703,0.006474557],"genre_scores_gemma":[0.02724413,0.0002916107,0.01816858,0.0002167248,0.00004800198,0.0002329199,0.9518871,0.0001965359,0.001714379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05439519,"threshold_uncertainty_score":0.1081572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1071454944889778,"score_gpt":0.3625074726673969,"score_spread":0.2553619781784191,"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."}}