{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002603123,0.0002430542,0.0004456712,0.0008249977,0.0008706612,0.0001762342,0.002574333,0.0001915056,0.0007253838],"category_scores_gemma":[0.0002931383,0.0002674309,0.0002035836,0.001388336,0.000647225,0.001726596,0.001709441,0.000642276,0.00003328937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001804989,"about_ca_system_score_gemma":0.0002305414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004274685,"about_ca_topic_score_gemma":0.0001897765,"domain_scores_codex":[0.996317,0.0001543614,0.0003088875,0.0007922096,0.001459221,0.0009683172],"domain_scores_gemma":[0.9970513,0.0008983913,0.0001702661,0.000924804,0.0007284397,0.0002267931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001470812,0.0006060135,0.007334721,0.00008961992,0.000183176,0.0009407796,0.03828337,0.0002429817,0.02779513,0.09853285,0.6443498,0.1814945],"study_design_scores_gemma":[0.00437127,0.0007352506,0.004877986,0.0006263676,0.00004608051,0.00007117329,0.08153083,0.02347084,0.01582668,0.7244073,0.1426735,0.001362765],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5795098,0.001138541,0.3840406,0.01238974,0.001361974,0.0005605557,0.000003559433,0.000599345,0.02039588],"genre_scores_gemma":[0.9505913,0.00008544559,0.0464225,0.00004554646,0.0004970284,0.000002344814,0.000005786802,0.00001309963,0.002336984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6258744,"threshold_uncertainty_score":0.9999778,"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."}}