{"id":"W2159833638","doi":"10.5555/1182635.1164155","title":"Putting context into schema matching","year":2006,"lang":"en","type":"article","venue":"Edinburgh Research Explorer (University of Edinburgh)","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Schema matching; Computer science; Schema (genetic algorithms); Schema migration; Star schema; Database schema; Conceptual schema; Matching (statistics); Optimal matching; Information schema; Schema evolution; Data mining; Semi-structured model; Data integration; Data exchange; Information retrieval; Database; Mathematics; Gender schema theory; Database design","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":[0.009966208,0.0007919687,0.0009896174,0.003430908,0.00228472,0.00638675,0.002501283,0.002254361,0.004687808],"category_scores_gemma":[0.03313883,0.0008707091,0.001420174,0.004403661,0.004592078,0.01702177,0.009684396,0.003233915,0.0009582782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001354518,"about_ca_system_score_gemma":0.002356967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003923424,"about_ca_topic_score_gemma":0.003948173,"domain_scores_codex":[0.9872516,0.00651272,0.001079069,0.002253744,0.00225817,0.0006445256],"domain_scores_gemma":[0.9847206,0.006758825,0.0007622058,0.005915768,0.00144187,0.0004007369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001327961,0.00005720411,0.002586911,0.0003716059,0.0001000085,0.0003867807,0.002235158,0.007457253,0.003934687,0.7940408,0.004541097,0.1841558],"study_design_scores_gemma":[0.00003965224,0.00006710549,0.0007351942,0.0003403716,0.00011404,0.0007747073,0.001168153,0.02850806,0.00759726,0.8312987,0.1292892,0.0000675645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01617962,0.002642454,0.9623405,0.003721383,0.000418272,0.0002031576,0.0002470496,0.001440876,0.01280653],"genre_scores_gemma":[0.1751291,0.002117923,0.8175358,0.001221322,0.0002518091,0.0001789597,0.0005851466,0.0004691812,0.00251076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009966208,"threshold_uncertainty_score":0.05270696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06575477420670861,"score_gpt":0.2972301425170633,"score_spread":0.2314753683103546,"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."}}