{"id":"W2076039115","doi":"10.1109/tkde.2009.25","title":"Evaluating the Generation of Domain Ontologies in the Knowledge Puzzle Project","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Topic Modeling","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Ontology; Computer science; Ontology learning; Upper ontology; Information retrieval; Domain (mathematical analysis); Ontology-based data integration; Domain knowledge; Process ontology; Suggested Upper Merged Ontology; Set (abstract data type); Ontology alignment; Natural language processing; Data science; Artificial intelligence; Semantic Web","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":[0.01135277,0.001083707,0.0008952143,0.002574671,0.0008788874,0.002325331,0.001997393,0.001963608,0.002879117],"category_scores_gemma":[0.06540478,0.0004931436,0.00058971,0.002676472,0.00118307,0.004852819,0.003503022,0.001240607,0.0007559828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001825307,"about_ca_system_score_gemma":0.001643117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005153508,"about_ca_topic_score_gemma":0.005120262,"domain_scores_codex":[0.9895079,0.005993048,0.0008969897,0.001039462,0.002334165,0.0002285652],"domain_scores_gemma":[0.941774,0.04761125,0.00151219,0.003718784,0.004642796,0.0007411059],"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":[0.004314326,0.003838238,0.01834477,0.003120194,0.0004362896,0.001627328,0.006198972,0.1660923,0.01857604,0.01264454,0.02278239,0.7420247],"study_design_scores_gemma":[0.001474552,0.003632907,0.02652038,0.0004029546,0.0003186991,0.001302909,0.006953436,0.8196582,0.07659437,0.01328424,0.04966199,0.0001954269],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8988647,0.000856367,0.07929979,0.0006158837,0.0001277892,0.001342615,0.002410328,0.004623787,0.01185873],"genre_scores_gemma":[0.6746054,0.0005144653,0.304245,0.0002202565,0.00003265066,0.001032556,0.01423028,0.0006784412,0.004440981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01135277,"threshold_uncertainty_score":0.06003994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1546157879818343,"score_gpt":0.3661375498068649,"score_spread":0.2115217618250305,"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."}}