{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007253206,0.0004805729,0.0007700884,0.006095952,0.002481554,0.004576083,0.001638588,0.001282324,0.003091675],"category_scores_gemma":[0.03592511,0.0004778579,0.001238335,0.01008926,0.00251637,0.01450751,0.002122151,0.002177181,0.0007692039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001580961,"about_ca_system_score_gemma":0.001687578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003058187,"about_ca_topic_score_gemma":0.002433286,"domain_scores_codex":[0.9929888,0.003016167,0.0006544621,0.001024568,0.002169351,0.0001466837],"domain_scores_gemma":[0.9651812,0.02626278,0.001077441,0.003675722,0.003544349,0.0002585048],"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":[0.0001074823,0.0001903441,0.004367196,0.0008968871,0.00008103251,0.0005838578,0.003473028,0.003844866,0.007500242,0.3693691,0.006921234,0.6026647],"study_design_scores_gemma":[0.00005841168,0.0002094901,0.007244606,0.001015298,0.0002013226,0.004189321,0.004100234,0.219465,0.02974593,0.5119355,0.2216668,0.000168109],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02317222,0.004008022,0.9600342,0.001648861,0.0001858604,0.0002127208,0.0001448001,0.0003131398,0.01028013],"genre_scores_gemma":[0.1940995,0.004837255,0.7938467,0.0005573264,0.0002455007,0.0002399718,0.0008680911,0.0002114821,0.005094193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007253206,"threshold_uncertainty_score":0.03835905,"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."}}