{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001233533,0.00007156109,0.0002355177,0.000209478,0.000148613,0.0001819112,0.0008017399,0.00002028683,0.000002371997],"category_scores_gemma":[0.00003591954,0.00004575264,0.00005824039,0.0002820302,0.0002271379,0.0007506239,0.0001539367,0.00009569909,0.000001881343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004394868,"about_ca_system_score_gemma":0.0001826019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001587114,"about_ca_topic_score_gemma":0.00002203123,"domain_scores_codex":[0.9989113,0.00005363338,0.0004098257,0.0001342812,0.0003516383,0.0001393722],"domain_scores_gemma":[0.9989558,0.0002603388,0.0003545503,0.0001903789,0.0001696853,0.00006917874],"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":[0.000009277897,0.0001621579,0.07185104,0.0003301192,0.00008055596,0.00009200675,0.00194567,0.0002638756,0.01160237,0.8562192,0.002785776,0.05465794],"study_design_scores_gemma":[0.001695284,0.001632806,0.1570458,0.001097198,0.00008942309,0.009173659,0.002242106,0.8081608,0.005039546,0.004772071,0.008385857,0.0006653974],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5703392,0.0002170371,0.4260212,0.000437842,0.002051679,0.00003993655,8.893608e-7,0.0000185913,0.0008736075],"genre_scores_gemma":[0.8891948,0.00001339974,0.1106018,0.00002848106,0.0001525289,2.441024e-7,5.677267e-8,0.000001092466,0.00000758755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8514472,"threshold_uncertainty_score":0.1865739,"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."}}