{"id":"W1993188540","doi":"10.1142/s0218194007003446","title":"XML SCHEMA MATCHING","year":2007,"lang":"en","type":"article","venue":"International Journal of Software Engineering and Knowledge Engineering","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Open Text (Canada); University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Schema matching; Document Structure Description; RELAX NG; XML Schema Editor; XML validation; XML Schema (W3C); XML; Star schema; Efficient XML Interchange; Information retrieval; XML database; Matching (statistics); Schema (genetic algorithms); Data mining; Streaming XML; Database; Data integration; Document type definition; Mathematics; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005895771,0.001133665,0.001643326,0.00529923,0.002093298,0.005523636,0.003969609,0.003305679,0.02610219],"category_scores_gemma":[0.02393315,0.0008464036,0.002828695,0.009383951,0.000927681,0.01121749,0.005568805,0.001995972,0.01276233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001839436,"about_ca_system_score_gemma":0.003716859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00352963,"about_ca_topic_score_gemma":0.002783067,"domain_scores_codex":[0.9910477,0.002135385,0.001544355,0.001801236,0.002990623,0.0004806577],"domain_scores_gemma":[0.9907109,0.002709375,0.0006036232,0.003277273,0.002411025,0.0002877602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003720915,0.000384768,0.003129549,0.001424306,0.0003719883,0.0008617569,0.001007951,0.01437929,0.009873381,0.2347002,0.1156833,0.6178116],"study_design_scores_gemma":[0.0001422728,0.0001560701,0.001172028,0.0004654475,0.0002071678,0.001868013,0.001172037,0.1068149,0.02232494,0.2802596,0.5853075,0.0001099976],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006900441,0.001593549,0.9424831,0.001868092,0.0005284267,0.001371647,0.008072061,0.009445755,0.02773692],"genre_scores_gemma":[0.05533796,0.001894116,0.8940002,0.001461508,0.0001598007,0.0005926528,0.02690617,0.001604564,0.01804293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02610219,"threshold_uncertainty_score":0.08732051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006121626440767754,"score_gpt":0.2400897564440809,"score_spread":0.2339681300033132,"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."}}