{"id":"W2098960900","doi":"10.1109/cisis.2009.40","title":"Creating Visualizations through Ontology Mapping","year":2009,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Ontology; Ontology-based data integration; Visualization; Process ontology; Domain (mathematical analysis); Upper ontology; Suggested Upper Merged Ontology; Ontology engineering; Model transformation; Software engineering; Data transformation; Information retrieval; Data mining; Domain knowledge; Data warehouse; Artificial intelligence","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":[0.003077312,0.00143696,0.0008571383,0.00379187,0.001046198,0.005025747,0.001466816,0.001219673,0.00900825],"category_scores_gemma":[0.01538246,0.0008591993,0.001705068,0.002560775,0.001000342,0.007917903,0.004732953,0.002008369,0.001547283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005830586,"about_ca_system_score_gemma":0.0008921198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001788554,"about_ca_topic_score_gemma":0.002249928,"domain_scores_codex":[0.9986184,0.0005227677,0.0001431025,0.0002364858,0.0003907061,0.00008863574],"domain_scores_gemma":[0.9932943,0.00327963,0.0003353599,0.001896613,0.0009455503,0.0002485821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005557773,0.0004504034,0.008768384,0.001674004,0.0003596313,0.001892033,0.0144842,0.04349376,0.05105013,0.2935373,0.0700343,0.5137001],"study_design_scores_gemma":[0.0001915838,0.0001298257,0.002630985,0.0006602424,0.0001566188,0.0009550821,0.002554702,0.2926812,0.05601671,0.3386891,0.3050516,0.0002824001],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01277858,0.0001739637,0.9608601,0.0008041316,0.0001452838,0.0001097023,0.001225836,0.01683044,0.007071909],"genre_scores_gemma":[0.1082894,0.0005074853,0.8818909,0.0001944387,0.00004330809,0.0003353504,0.002807836,0.003627402,0.002303824],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00900825,"threshold_uncertainty_score":0.03013563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04216284259050625,"score_gpt":0.3123481160945338,"score_spread":0.2701852735040275,"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."}}