{"id":"W2404837633","doi":"","title":"Conjunctive query answering in distributed ontology systems for ontologies with large OWL ABoxes","year":2009,"lang":"en","type":"article","venue":"Research Publications (Maastricht University)","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Ontology; Conjunctive query; Information retrieval; Web Ontology Language; Web query classification; Query language; Query optimization; Query expansion; Sargable; Set (abstract data type); Web search query; Construct (python library); Semantic Web; Search engine; Programming language; Relational database","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":[0.009777064,0.0006218229,0.00124486,0.001143482,0.001950703,0.004531462,0.002339363,0.001427906,0.002848975],"category_scores_gemma":[0.02216809,0.0008469832,0.001548002,0.001933887,0.002986965,0.006091294,0.004391865,0.002208225,0.0007276308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002199489,"about_ca_system_score_gemma":0.001969753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008708893,"about_ca_topic_score_gemma":0.007424559,"domain_scores_codex":[0.9895745,0.003544892,0.0009080156,0.001899005,0.003437535,0.0006360422],"domain_scores_gemma":[0.9800773,0.0143724,0.0008859482,0.002457253,0.001843756,0.0003633022],"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":[0.001566453,0.001007778,0.006562015,0.001150593,0.0006144543,0.004888601,0.008140841,0.1669322,0.04869038,0.4395252,0.01857983,0.3023416],"study_design_scores_gemma":[0.0002696255,0.000133094,0.0008047427,0.00006052604,0.0002224806,0.000662118,0.001139123,0.7474874,0.02181922,0.2164901,0.01085272,0.00005878745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02832406,0.0001722251,0.9673548,0.0005151066,0.00003529914,0.0002193945,0.0001458668,0.001453046,0.001780155],"genre_scores_gemma":[0.3499078,0.0001571284,0.6458163,0.0004167072,0.000100092,0.0003188576,0.0006401524,0.0002476252,0.002395386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009777064,"threshold_uncertainty_score":0.05170667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06383734209559637,"score_gpt":0.3199586848347136,"score_spread":0.2561213427391172,"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."}}