{"id":"W2372347460","doi":"","title":"A Study on Taxonomic Relation Extraction from Ontology Learning","year":2007,"lang":"en","type":"article","venue":"Computer Technology and Development","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Ontology learning; Computer science; Ontology; Relation (database); Taxonomy (biology); Process ontology; Ontology-based data integration; Open Biomedical Ontologies; Upper ontology; Suggested Upper Merged Ontology; Information retrieval; Ontology components; Domain (mathematical analysis); Relationship extraction; Ontology alignment; Artificial intelligence; Natural language processing; Information extraction; Semantic Web; Data mining; Ecology; Mathematics; Epistemology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001855865,0.000115733,0.0001187704,0.0003589592,0.0002623625,0.00002795059,0.0002558017,0.0001144267,0.000001608053],"category_scores_gemma":[0.000006955633,0.0001170691,0.00001123275,0.0002889728,0.00003479978,0.0001248012,0.0002080866,0.0002593234,0.00003440578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007153818,"about_ca_system_score_gemma":0.00002920497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000205396,"about_ca_topic_score_gemma":0.000005900775,"domain_scores_codex":[0.9990649,0.00001879632,0.0002461421,0.0004170185,0.00008744412,0.0001657277],"domain_scores_gemma":[0.999484,0.0001217584,0.0001074149,0.0002099176,0.00004049174,0.0000364316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001265852,0.000257536,0.01701598,0.000001294996,0.00003287392,0.000032412,0.000683713,0.0006573547,0.0002504136,0.1578707,0.00007223945,0.8231128],"study_design_scores_gemma":[0.001089367,0.0007706408,0.8335505,0.0000363436,0.000007352306,0.00009519514,0.0003122373,0.01625721,0.006615805,0.07025293,0.07041778,0.0005946649],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2613864,0.0000227822,0.7372579,0.0003728759,0.00008144241,0.0001866371,9.494287e-8,0.0004811755,0.0002107392],"genre_scores_gemma":[0.5816896,0.000001825256,0.4181455,0.00009304448,0.00001667203,0.00003068232,0.000003018397,0.000003191871,0.00001643],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8225182,"threshold_uncertainty_score":0.4773939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02724228910057934,"score_gpt":0.3000879342284054,"score_spread":0.2728456451278261,"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."}}