{"id":"W2442049918","doi":"10.1145/2882903.2882944","title":"Query Planning for Evaluating SPARQL Property Paths","year":2016,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"SPARQL; Computer science; Query optimization; RDF query language; RDF; Query plan; Property (philosophy); Query language; Plan (archaeology); Graph; Query expansion; Sargable; Information retrieval; Database; Web query classification; Theoretical computer science; Web search query; Search engine; Semantic Web","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":[0.0004168309,0.00007062016,0.00009439218,0.00002947652,0.00008169865,0.00005993545,0.0004045181,0.00003093346,0.00001412145],"category_scores_gemma":[0.0003334526,0.00002834878,0.00003727913,0.00005301062,0.00001978634,0.0002882267,0.0001216772,0.00002008289,0.00003767792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001524294,"about_ca_system_score_gemma":0.00005387769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001891399,"about_ca_topic_score_gemma":0.000004557887,"domain_scores_codex":[0.9992563,0.00002504197,0.0001264221,0.000238642,0.0001306551,0.0002229108],"domain_scores_gemma":[0.9992836,0.0003055946,0.00003894174,0.0002781255,0.00006157866,0.00003212494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001527822,0.00002970366,0.008689814,0.00002390321,0.00001660278,0.000008571235,0.0006871608,0.00001934323,0.01746291,0.04985629,0.01155918,0.9116312],"study_design_scores_gemma":[0.006234627,0.002454551,0.05936537,0.001016267,0.00004672863,0.0001296455,0.001042867,0.5444035,0.1609,0.1346664,0.08758701,0.002153039],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0271927,0.00006642527,0.9627216,0.002786923,0.0003043087,0.000181705,4.094e-7,0.0002608851,0.006484971],"genre_scores_gemma":[0.7455019,0.000002337113,0.2487464,0.0004827647,0.00007533211,0.00004544042,1.914466e-7,0.000005190972,0.005140409],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9094782,"threshold_uncertainty_score":0.115603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.124938954538063,"score_gpt":0.3477191710025837,"score_spread":0.2227802164645207,"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."}}