{"id":"W2067415764","doi":"10.1109/fuzz-ieee.2013.6622491","title":"A FML-based hybrid reasoner combining fuzzy ontology and Mamdani inference","year":2013,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Semantic reasoner; Computer science; Neuro-fuzzy; Ontology; Artificial intelligence; Fuzzy logic; Adaptive neuro fuzzy inference system; Fuzzy classification; Fuzzy set operations; Data mining; Fuzzy control system; Machine learning; Information retrieval","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":[0.001742292,0.0007698092,0.001266396,0.001337385,0.0007905697,0.001742391,0.001775093,0.001535372,0.004314833],"category_scores_gemma":[0.002556084,0.0004909334,0.00154621,0.0007981585,0.0005593341,0.00168159,0.001031895,0.00114648,0.001663595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001327281,"about_ca_system_score_gemma":0.001443944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01024497,"about_ca_topic_score_gemma":0.01220591,"domain_scores_codex":[0.9990333,0.0001889091,0.0001127347,0.0002276726,0.0003720346,0.00006521634],"domain_scores_gemma":[0.9993355,0.0002961317,0.00004694343,0.0001034596,0.0001924029,0.00002553322],"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":[0.0003772781,0.0003917103,0.00168446,0.000839221,0.0004283759,0.0009104837,0.0005941804,0.2353771,0.02872514,0.06033397,0.01651977,0.6538183],"study_design_scores_gemma":[0.0000530492,0.00004472997,0.0003093145,0.00004763011,0.00007830704,0.0001922621,0.00004824389,0.9628856,0.008404004,0.01379216,0.01410352,0.00004125771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003384866,0.0001187907,0.9894224,0.0001977398,0.00004126343,0.0001109988,0.0002372579,0.004675242,0.001811326],"genre_scores_gemma":[0.08925633,0.0001125124,0.9065555,0.0002822577,0.00003144975,0.0001955769,0.0005471095,0.0001331685,0.002886035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01024497,"threshold_uncertainty_score":0.02037066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01704135266853088,"score_gpt":0.2418803205911425,"score_spread":0.2248389679226116,"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."}}