{"id":"W2900966416","doi":"10.5815/ijieeb.2018.06.01","title":"Towards Semantic Geo/BI: A Novel Approach for Semantically Enriching Geo/BI Data with OWL Ontological Layers (OOLAP and ODW) to Enable Semantic Exploration, Analysis and Discovery of Geospatial Business Intelligence Knowledge","year":2018,"lang":"en","type":"article","venue":"International Journal of Information Engineering and Electronic Business","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; West African Science Service Centre on Climate Change and Adapted Land Use; Université Laval","keywords":"Computer science; Online analytical processing; Geospatial analysis; Semantic computing; Semantic analytics; Data warehouse; Semantic Web; Semantic technology; Semantic grid; Semantic data model; Business intelligence; Data science; Information retrieval; Semantic Web Stack; Knowledge management; Data mining; Geology; Remote sensing","routes":{"ca_aff":true,"ca_fund":true,"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":[0.007304735,0.0009275105,0.0008535791,0.004507163,0.001801836,0.007550184,0.002465587,0.00134573,0.001224841],"category_scores_gemma":[0.008604998,0.001035218,0.001965405,0.006588807,0.003243615,0.01707218,0.01114835,0.0040098,0.0007859101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001749166,"about_ca_system_score_gemma":0.004672963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009830019,"about_ca_topic_score_gemma":0.01157635,"domain_scores_codex":[0.9952573,0.001465456,0.0006185934,0.0006511578,0.001705883,0.00030157],"domain_scores_gemma":[0.9951643,0.0009732731,0.0004618078,0.002230944,0.0008459628,0.0003236646],"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":[0.00009000249,0.0001890704,0.002917847,0.0006679198,0.0002048384,0.0008433351,0.004141619,0.007419558,0.01011909,0.7989812,0.0103934,0.1640322],"study_design_scores_gemma":[0.00002887762,0.00005700515,0.001351162,0.0005557716,0.0002008394,0.0009726656,0.00308228,0.1285435,0.01806301,0.5353245,0.3116722,0.000148241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00347958,0.0002345316,0.9894354,0.001033348,0.00009899713,0.0001169471,0.0003732277,0.0007717604,0.004456169],"genre_scores_gemma":[0.04764116,0.0005726613,0.9478793,0.0004709941,0.00004500831,0.0001591768,0.001124592,0.0001861043,0.001921006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009830019,"threshold_uncertainty_score":0.03863156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01890648633927986,"score_gpt":0.2536446782757705,"score_spread":0.2347381919364907,"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."}}