{"id":"W2142963442","doi":"10.1177/0165551508100383","title":"An empirical comparison of ontology matching techniques","year":2009,"lang":"en","type":"article","venue":"Journal of Information Science","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Ministry of Education, Libya","keywords":"Computer science; Matching (statistics); Ontology alignment; Ontology; Task (project management); Process (computing); Feature (linguistics); Data mining; Quality (philosophy); Feature matching; Information retrieval; Machine learning; Artificial intelligence; Feature extraction; Semantic Web; Ontology-based data integration; 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":[0.02298976,0.0008046953,0.0008153722,0.01311414,0.00127909,0.00231971,0.001907387,0.00176672,0.003085345],"category_scores_gemma":[0.164803,0.0003440921,0.001183377,0.01452225,0.0009805649,0.005834092,0.002470057,0.001015268,0.00132451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001246566,"about_ca_system_score_gemma":0.001052958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002204212,"about_ca_topic_score_gemma":0.002787093,"domain_scores_codex":[0.9623278,0.01524918,0.003749168,0.003934558,0.01394294,0.0007962603],"domain_scores_gemma":[0.8112839,0.1488212,0.007648662,0.01481358,0.01646936,0.0009631248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001607522,0.0009687948,0.09720678,0.004188376,0.001398479,0.0003134291,0.00199795,0.01836129,0.008078281,0.007646744,0.01501105,0.8432214],"study_design_scores_gemma":[0.0005950882,0.003765925,0.3928699,0.002081975,0.003053528,0.007428411,0.01205483,0.3782196,0.04367568,0.03251743,0.1233411,0.0003966286],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7577694,0.02786542,0.1668013,0.001434289,0.000585113,0.001037669,0.006608833,0.005017922,0.03288012],"genre_scores_gemma":[0.8481396,0.004914346,0.1341978,0.0001627003,0.0001252417,0.0003186563,0.008893391,0.0007175301,0.002530661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02298976,"threshold_uncertainty_score":0.1215829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03618679243024425,"score_gpt":0.3907106206773219,"score_spread":0.3545238282470777,"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."}}