{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006465437,0.0009435865,0.001286853,0.002602815,0.001154144,0.004231533,0.00330499,0.002440982,0.005597431],"category_scores_gemma":[0.03249547,0.001074002,0.001890585,0.002651701,0.00188918,0.01018454,0.004265396,0.003002727,0.002108515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001620905,"about_ca_system_score_gemma":0.001869491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007240842,"about_ca_topic_score_gemma":0.006052153,"domain_scores_codex":[0.9933748,0.003368927,0.00051321,0.001298336,0.001115358,0.0003293489],"domain_scores_gemma":[0.9811828,0.01415622,0.0005231279,0.002476339,0.001390599,0.0002710166],"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":[0.0004659726,0.0004956805,0.00374847,0.001950293,0.000364647,0.000404452,0.00236896,0.1648499,0.00937726,0.2246544,0.02306938,0.5682505],"study_design_scores_gemma":[0.00004903931,0.00008291614,0.0006528812,0.0001713316,0.0001006314,0.0001971875,0.0003307926,0.6059222,0.00672271,0.3613022,0.02442839,0.00003970677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01226182,0.0009982993,0.9774239,0.001435195,0.00006566902,0.0002749177,0.0007667118,0.003933348,0.002840166],"genre_scores_gemma":[0.26573,0.001421546,0.7230825,0.001021039,0.0001914258,0.000554246,0.003826195,0.0003449666,0.003828107],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007240842,"threshold_uncertainty_score":0.03419292,"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."}}