{"id":"W4213246264","doi":"10.1016/j.jcss.2010.04.009","title":"Foundations of Semantic Web databases","year":2010,"lang":"en","type":"article","venue":"Journal of Computer and System Sciences","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":102,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; RDF query language; RDF; Query language; SPARQL; RDF Schema; Semantic Web; Containment (computer programming); RDF/XML; Information retrieval; Logical consequence; Query optimization; Database; Web query classification; Programming language; Web search query; 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":[0.006015097,0.0007694598,0.001535688,0.005996839,0.002967773,0.01519565,0.00321062,0.003208419,0.006386463],"category_scores_gemma":[0.01283936,0.001773798,0.001692541,0.008864591,0.006931053,0.02333612,0.005941956,0.005621481,0.002045962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002997836,"about_ca_system_score_gemma":0.00355564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003519573,"about_ca_topic_score_gemma":0.002129303,"domain_scores_codex":[0.9934565,0.001811439,0.0009389424,0.001045239,0.002419495,0.0003283721],"domain_scores_gemma":[0.9905478,0.005021771,0.0004681129,0.002323085,0.001270276,0.0003688565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00000784095,0.00001833097,0.00008459122,0.00006523867,0.00001373003,0.00003951978,0.0001312065,0.0006734177,0.00009018576,0.9862432,0.001442272,0.01119052],"study_design_scores_gemma":[0.000005865526,0.000003738923,0.00004662709,0.00005835622,0.00001157271,0.000055429,0.00005398984,0.003807741,0.000138808,0.975086,0.02072576,0.000006155525],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009990916,0.02078734,0.8728555,0.01486874,0.001001637,0.000194601,0.0009239708,0.0008064817,0.07857075],"genre_scores_gemma":[0.3880582,0.03364091,0.5456569,0.004256942,0.004224052,0.0006459564,0.002945549,0.0003801711,0.02019122],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01519565,"threshold_uncertainty_score":0.03181124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03716863265475034,"score_gpt":0.2944975212328642,"score_spread":0.2573288885781139,"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."}}