{"id":"W3093907107","doi":"10.1145/3340531.3412164","title":"Neural Relation Extraction on Wikipedia Tables for Augmenting Knowledge Graphs","year":2020,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Knowledge graph; Relationship extraction; Table (database); Information retrieval; Task (project management); Information extraction; Question answering; Graph; Artificial neural network; Relation (database); Artificial intelligence; Knowledge extraction; Natural language processing; Machine learning; Data mining; Theoretical computer science","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":[0.0005471731,0.001125852,0.0005607602,0.0055802,0.0006723493,0.00121388,0.001178793,0.0009555248,0.004063442],"category_scores_gemma":[0.004329084,0.0004368952,0.001313337,0.00425464,0.0003583519,0.004077234,0.001087107,0.001332465,0.002684759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007200773,"about_ca_system_score_gemma":0.0010895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009091173,"about_ca_topic_score_gemma":0.02675805,"domain_scores_codex":[0.9993547,0.0001134589,0.0000492962,0.0002967124,0.0001381778,0.00004765996],"domain_scores_gemma":[0.9981819,0.0010508,0.0001262984,0.0003397003,0.0002487389,0.00005260068],"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":[0.0003020155,0.0004164412,0.009291821,0.001127902,0.0002903053,0.001172249,0.0006595853,0.05120859,0.0233767,0.01996942,0.08767881,0.8045061],"study_design_scores_gemma":[0.00006790351,0.0001529336,0.007775086,0.0003406053,0.0003743021,0.0008272192,0.0005499438,0.7549972,0.03245613,0.08909152,0.1132775,0.00008957299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0997478,0.005374528,0.8183978,0.001339806,0.0006779486,0.00046907,0.02783722,0.02936536,0.01679051],"genre_scores_gemma":[0.4154041,0.002516644,0.5038674,0.0004854181,0.0002613308,0.0003137345,0.06813832,0.0007828809,0.008230156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009091173,"threshold_uncertainty_score":0.01807648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06171215666897496,"score_gpt":0.2914301961318552,"score_spread":0.2297180394628802,"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."}}