{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005938893,0.0001522095,0.0001862206,0.0003676417,0.00003035311,0.0000670018,0.0003938214,0.00004862461,0.000002240764],"category_scores_gemma":[0.0003641324,0.0001438661,0.00007082868,0.0001528949,0.0000096747,0.000748101,0.0001730419,0.0002513296,0.00000434046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007788106,"about_ca_system_score_gemma":0.00003488621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002915387,"about_ca_topic_score_gemma":0.000001024654,"domain_scores_codex":[0.998986,0.000004780269,0.0004115542,0.0001356131,0.0002523213,0.000209732],"domain_scores_gemma":[0.9990799,0.0002668519,0.0001187059,0.0001273485,0.0002658339,0.000141312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007793897,0.0002357184,0.003283003,0.0005643787,0.0009851896,0.001888984,0.007607541,0.1518824,0.0579592,0.4674037,0.0005496095,0.3075624],"study_design_scores_gemma":[0.005459506,0.0004649654,0.02614606,0.005367742,0.00006821903,0.01245468,0.0004995671,0.2312478,0.06026113,0.0007955479,0.6544713,0.002763556],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05146725,0.001800057,0.9438932,0.00003602408,0.002607711,0.00002598018,0.000002384666,0.0001276059,0.00003978622],"genre_scores_gemma":[0.5311289,0.00005422645,0.4679963,0.00001158651,0.0007444655,0.000001011882,0.000001138335,0.00001884643,0.00004348251],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6539217,"threshold_uncertainty_score":0.586669,"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."}}