{"id":"W567586408","doi":"10.1007/978-3-319-17957-5_4","title":"Coping with Uncertainty in Schema Matching: Bayesian Networks and Agent-Based Modeling Approach","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in business information processing","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Schema matching; Schema (genetic algorithms); Computer science; Probabilistic logic; Matching (statistics); Bayesian network; Data integration; Artificial intelligence; Bayesian probability; Data mining; Machine learning; Mathematics","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":[0.0064162,0.0008152078,0.00175156,0.002721992,0.0009372236,0.004415948,0.004243646,0.002701129,0.002803708],"category_scores_gemma":[0.02653231,0.001648278,0.001810414,0.00455076,0.001515305,0.01077329,0.002973032,0.002768512,0.0004561982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001973438,"about_ca_system_score_gemma":0.001763446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01115334,"about_ca_topic_score_gemma":0.008691485,"domain_scores_codex":[0.9964361,0.001760632,0.0002436255,0.0005442429,0.0008569184,0.0001584384],"domain_scores_gemma":[0.9889346,0.008851163,0.0006991933,0.0007025397,0.0006119134,0.000200489],"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":[0.0001142116,0.0001581249,0.001797706,0.0001688449,0.0002881241,0.0001980865,0.0004234447,0.6714332,0.0005982976,0.2234187,0.002140753,0.0992606],"study_design_scores_gemma":[0.000006522367,0.000007526245,0.0001259353,0.00001737482,0.00003822249,0.00003679535,0.0000270578,0.8663967,0.0002060452,0.1323178,0.0008060297,0.00001404463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006676094,0.0006691586,0.989343,0.0005713443,0.00002867883,0.00003821557,0.00009412579,0.0001539716,0.002425383],"genre_scores_gemma":[0.3886552,0.00257023,0.6042241,0.0002581516,0.0001822465,0.00022506,0.0005545662,0.0001530769,0.003177346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01115334,"threshold_uncertainty_score":0.03393251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0276218506887488,"score_gpt":0.2389224847966524,"score_spread":0.2113006341079036,"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."}}