{"id":"W2963593153","doi":"10.1177/0165551519865495","title":"Knowledge discovery using SPARQL property path: The case of disease data set","year":2019,"lang":"en","type":"article","venue":"Journal of Information Science","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cape Breton University","funders":"","keywords":"SPARQL; Computer science; Named graph; Information retrieval; Ontology; Semantic Web; Path (computing); Linked data; Social Semantic Web; Graph; RDF; World Wide Web; Data mining; Theoretical computer science","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002283821,0.00006141956,0.0001174875,0.0001803076,0.0001448532,0.0004332058,0.002383506,0.00001338765,0.000003495833],"category_scores_gemma":[0.0005909603,0.00002719389,0.00003643857,0.0006775385,0.0002090399,0.01988825,0.0007134351,0.00009528054,0.00001582844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003660218,"about_ca_system_score_gemma":0.000923257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000303867,"about_ca_topic_score_gemma":0.000003009803,"domain_scores_codex":[0.998868,0.00003667488,0.0004621315,0.00008449311,0.0004060089,0.0001427274],"domain_scores_gemma":[0.9980347,0.0001069036,0.0005855847,0.0007597983,0.0004377104,0.00007532495],"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":[0.00056421,0.0008023997,0.07652616,0.001451178,0.0001699598,0.000876599,0.09949589,0.03977279,0.01454661,0.20247,0.01971953,0.5436047],"study_design_scores_gemma":[0.0004012119,0.0001163952,0.01262496,0.0001678742,0.00001701096,0.001778677,0.002545136,0.9768479,0.001057674,0.0005953702,0.003701794,0.0001459811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8019579,0.00009958776,0.1954764,0.0005702029,0.0008950316,0.0001272773,0.00001028857,0.000008201296,0.0008551033],"genre_scores_gemma":[0.992992,0.00001484349,0.006817235,0.0001258519,0.00002629223,1.898636e-7,4.601801e-7,8.531762e-7,0.00002227759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9370751,"threshold_uncertainty_score":0.9938201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08843087325629086,"score_gpt":0.3319612602807036,"score_spread":0.2435303870244127,"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."}}