{"id":"W2230459429","doi":"","title":"Leverage of OWL-DL axioms in a Contact Centre for Technical Product Support","year":2010,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; University of New Brunswick","funders":"","keywords":"Computer science; Web Ontology Language; OWL-S; Ontology; Axiom; Upper ontology; Semantic Web Rule Language; Semantic Web; Leverage (statistics); World Wide Web; Ontology language; Class (philosophy); Information retrieval; Database; Semantic Web Stack; Artificial intelligence; Semantic analytics","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":[0.003026696,0.0004155235,0.0003706499,0.001490257,0.001313702,0.003184808,0.001716171,0.0009614418,0.005655016],"category_scores_gemma":[0.006836859,0.0005139443,0.0007146812,0.001363914,0.001167397,0.006434718,0.002734339,0.001402661,0.002270211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001258901,"about_ca_system_score_gemma":0.00285258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006897179,"about_ca_topic_score_gemma":0.0107176,"domain_scores_codex":[0.99748,0.000511341,0.0002681601,0.0004023247,0.001214574,0.0001236624],"domain_scores_gemma":[0.9959036,0.001563353,0.0002203723,0.001422251,0.0007251286,0.0001653167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004310167,0.0006380018,0.006134744,0.0005074704,0.00009825226,0.003163894,0.003275977,0.01302337,0.03113098,0.5495487,0.03849434,0.3535532],"study_design_scores_gemma":[0.0001425704,0.0001167982,0.002088267,0.0001740975,0.0001988402,0.002074759,0.0007957606,0.2682084,0.136127,0.1202846,0.4696773,0.000111575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02263203,0.0000520475,0.9469951,0.0005982259,0.00006398333,0.0002606909,0.0008486836,0.01469011,0.01385911],"genre_scores_gemma":[0.2220933,0.0001504557,0.7631566,0.0003433755,0.00004173565,0.0001553178,0.002524347,0.001231391,0.01030352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006897179,"threshold_uncertainty_score":0.01891792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01626515582352805,"score_gpt":0.2649950185652001,"score_spread":0.248729862741672,"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."}}