{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004188244,0.0009974089,0.0007388467,0.001298593,0.0008377268,0.002409307,0.001412455,0.0007852681,0.007218134],"category_scores_gemma":[0.01274398,0.000735466,0.001539115,0.001623453,0.00128198,0.003910265,0.00217427,0.001649635,0.001245054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001802499,"about_ca_system_score_gemma":0.001986571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009312181,"about_ca_topic_score_gemma":0.01307029,"domain_scores_codex":[0.9946384,0.001727844,0.0004374228,0.0005834206,0.00230997,0.0003029478],"domain_scores_gemma":[0.9948985,0.00293264,0.0002652915,0.0008659284,0.0009327323,0.0001049983],"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":[0.0005793772,0.0002743482,0.004265893,0.0009222299,0.0002119011,0.0004351879,0.0008605758,0.3167548,0.01657366,0.2371529,0.02772193,0.3942471],"study_design_scores_gemma":[0.00006762225,0.00007064423,0.0004959081,0.00006558607,0.00005243874,0.0001176936,0.0002781172,0.8528843,0.01425793,0.1106184,0.02105015,0.000041246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009688165,0.0001512106,0.9772625,0.0003307418,0.00003436457,0.0002875508,0.0009463017,0.006349929,0.00494926],"genre_scores_gemma":[0.131471,0.0002315735,0.8611212,0.0001662246,0.00003419487,0.0004033486,0.002771474,0.00169346,0.002107604],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009312181,"threshold_uncertainty_score":0.02414703,"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."}}