{"id":"W2811102277","doi":"10.14569/ijacsa.2018.090620","title":"Generating Relational Database using Ontology Review","year":2018,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Relational database; Ontology; Database schema; USable; Database model; Relational model; Inference; Database; Knowledge extraction; Database design; Schema (genetic algorithms); Information retrieval; Data mining; World Wide Web; Artificial intelligence","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":[0.003468059,0.0004775811,0.000728955,0.004654938,0.001122099,0.00429219,0.001972463,0.0006669276,0.006569289],"category_scores_gemma":[0.009641517,0.0004517092,0.002081138,0.004613047,0.0005942072,0.00570555,0.002144917,0.001008734,0.003260047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001197148,"about_ca_system_score_gemma":0.002980292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004461044,"about_ca_topic_score_gemma":0.003723914,"domain_scores_codex":[0.9946025,0.001210872,0.0007349733,0.0007576279,0.002522935,0.0001710616],"domain_scores_gemma":[0.996235,0.0008779517,0.0002409156,0.0007840819,0.001744635,0.0001174919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001064208,0.0001293157,0.002868452,0.001666494,0.0001992308,0.0009517769,0.000961305,0.005915619,0.009700452,0.1988267,0.04662918,0.732045],"study_design_scores_gemma":[0.00004526354,0.00007771367,0.001683453,0.0005790657,0.0002108471,0.002117058,0.001129795,0.05336661,0.02018381,0.0674976,0.8530052,0.0001036561],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01036873,0.01048138,0.9456295,0.001896542,0.0006776504,0.0009865965,0.00323443,0.002801418,0.02392378],"genre_scores_gemma":[0.0740156,0.01230831,0.8899782,0.0004055261,0.0002067632,0.0005128899,0.009374537,0.0005074851,0.01269066],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006569289,"threshold_uncertainty_score":0.02197647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03421802743340294,"score_gpt":0.3473999532232925,"score_spread":0.3131819257898896,"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."}}