{"id":"W2973947483","doi":"10.1145/3345557","title":"Question Answering in Knowledge Bases","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Topic Modeling","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Dream Project of Ministry of Science and Technology of the People's Republic of China; Fundamental Research Funds for the Central Universities; Foundation for Innovative Research Groups of the National Natural Science Foundation of China; State Key Laboratory of Software Development Environment","keywords":"Computer science; Correctness; Question answering; Bottleneck; Knowledge base; Relation (database); Information bottleneck method; Artificial intelligence; Information retrieval; Machine learning; Data mining; Programming language","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003466617,0.00008995636,0.0001134884,0.0003614702,0.00005788518,0.0001679205,0.0004144112,0.00006241322,0.00001089171],"category_scores_gemma":[0.00001565622,0.00009003766,0.00003424982,0.0003555902,0.000005139439,0.002859918,0.000008622498,0.0001307518,0.0008399507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001195619,"about_ca_system_score_gemma":0.00004594404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001539061,"about_ca_topic_score_gemma":0.00001344621,"domain_scores_codex":[0.9991094,0.00005346349,0.0003866546,0.000124382,0.0001789769,0.0001471204],"domain_scores_gemma":[0.9991255,0.00008198915,0.00007622812,0.0006077478,0.00006959261,0.00003887373],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001532592,0.00009521942,0.001447698,0.0003485985,0.00001859518,0.000001089247,0.009412115,0.6609665,0.0002073538,0.05075104,0.00005983065,0.2766767],"study_design_scores_gemma":[0.0006347921,0.00006573444,0.001248858,0.0002510576,0.000002432934,0.00001852566,0.0007035508,0.9767904,0.0007039488,0.0001658316,0.01918058,0.0002343316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02507856,0.00002722616,0.9676965,0.0001331723,0.001378116,0.0003035589,0.000002706555,0.0001880304,0.005192105],"genre_scores_gemma":[0.9948971,0.000007841607,0.004734608,0.00006712925,0.00001750824,0.00005156025,0.000003183858,0.000003487371,0.0002175934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9698185,"threshold_uncertainty_score":0.999938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01802759280627467,"score_gpt":0.2547266321229922,"score_spread":0.2366990393167175,"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."}}