{"id":"W2169606199","doi":"","title":"Optimization techniques for retrieving resources described in OWL/RDF documents: first results","year":2004,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"RDF; Computer science; Description logic; Semantic Web; SPARQL; RDF Schema; Web Ontology Language; Semantic Web Rule Language; Semantic reasoner; Knowledge representation and reasoning; Cwm; Representation (politics); Information retrieval; Implementation; Inference; Linked data; Semantic Web Stack; Programming language; Semantic analytics; 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":[0.0004328114,0.0001034008,0.000136527,0.000156355,0.0001034206,0.0001850781,0.0005490432,0.00007641267,0.000003188835],"category_scores_gemma":[0.000469469,0.00008564006,0.00003726821,0.0004001066,0.00002901338,0.0005985251,0.0001394839,0.00005684055,0.000003132861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009198122,"about_ca_system_score_gemma":0.00003036096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003265019,"about_ca_topic_score_gemma":0.0002954635,"domain_scores_codex":[0.9989641,0.00001933772,0.0003004799,0.0003335575,0.0001431469,0.0002393784],"domain_scores_gemma":[0.999329,0.0001472939,0.00007757803,0.0003484992,0.00006562775,0.00003196947],"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":[0.001479903,0.001600469,0.01325249,0.0005663076,0.0001521903,0.0002123747,0.04148501,0.2806841,0.001919682,0.4343746,0.02732461,0.1969482],"study_design_scores_gemma":[0.01912837,0.002955393,0.01538307,0.002403244,0.00005193878,0.00009114017,0.003073863,0.4662268,0.2428939,0.1424875,0.1019138,0.003390995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004545628,0.00007834873,0.9845883,0.004161807,0.000102857,0.0003643699,0.000001172635,0.0004502399,0.005707298],"genre_scores_gemma":[0.3394113,0.00005228407,0.6599535,0.0002656415,0.00003077892,0.00003516283,0.000003230832,0.000005673667,0.0002424144],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3348657,"threshold_uncertainty_score":0.34923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01879382571074511,"score_gpt":0.2572870273626267,"score_spread":0.2384932016518815,"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."}}