{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007045065,0.0002062349,0.0004015702,0.000655488,0.00009004715,0.000421813,0.0007525042,0.00007549143,9.902926e-7],"category_scores_gemma":[0.0005301537,0.0001558356,0.00004021278,0.000843554,0.00008218543,0.003756779,0.0003285815,0.0001522988,4.29281e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005667585,"about_ca_system_score_gemma":0.0002637513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001515134,"about_ca_topic_score_gemma":0.0000904662,"domain_scores_codex":[0.9984358,0.00001673093,0.0006448486,0.0002387247,0.0003646036,0.0002992883],"domain_scores_gemma":[0.9976637,0.0001571985,0.0003424533,0.0002453089,0.001512464,0.00007892327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002236158,0.001273562,0.00798598,0.002753555,0.00704341,0.00003109037,0.02300748,0.5646546,0.008734331,0.1421195,0.0003310979,0.2398292],"study_design_scores_gemma":[0.0007065585,0.0002782554,0.02521951,0.0001818605,0.0001934535,0.0003115889,0.000304765,0.9710582,0.0007770966,0.0003606578,0.0003339446,0.0002740572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1492067,0.0002055041,0.8494387,0.0007535266,0.0001941342,0.0001419025,0.000009054111,0.0000253432,0.00002507457],"genre_scores_gemma":[0.932138,0.0002174272,0.06739916,0.00005917808,0.0001333144,0.000007418789,0.00003094186,0.000007045066,0.000007473402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7829313,"threshold_uncertainty_score":0.6354792,"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."}}