{"id":"W2139501711","doi":"10.71781/9668","title":"Intégration des connaissances ontologiques dans la fouille de motifs séquentiels avec application à la personnalisation Web","year":2008,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Abstraction; Ontology; Semantics (computer science); Set (abstract data type); Object (grammar); Scalability; Syntax; Artificial intelligence; Natural language processing; Programming language; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.008141495,0.0006426072,0.0005904245,0.0002676889,0.001332828,0.001465039,0.002877035,0.0007033217,0.00004864957],"category_scores_gemma":[0.001643363,0.0007316305,0.0003202488,0.0009052588,0.001975705,0.001024506,0.001325204,0.0009719045,0.00009098362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005327819,"about_ca_system_score_gemma":0.001088908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005503082,"about_ca_topic_score_gemma":0.004978561,"domain_scores_codex":[0.9866935,0.009351715,0.0009913153,0.00161864,0.0006519069,0.0006929066],"domain_scores_gemma":[0.9905218,0.002596261,0.001087343,0.002880024,0.002552404,0.0003621913],"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":[0.000008661031,0.0009478285,0.003639173,0.000166628,0.00009906942,0.00001295263,0.05918094,0.0004016579,0.005545958,0.6131518,0.001575561,0.3152698],"study_design_scores_gemma":[0.0007154529,0.000003580407,0.0408332,0.001920581,0.0001120599,0.0003193446,0.001036254,0.8272364,0.02447778,0.0239727,0.07829693,0.001075776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09653583,0.001117364,0.8448954,0.007708817,0.0001850611,0.0007346518,0.0001743989,0.000507702,0.0481408],"genre_scores_gemma":[0.6653478,0.004384269,0.3238424,0.0001041877,0.00007617108,0.000561878,0.0009337298,0.00005801613,0.004691603],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8268347,"threshold_uncertainty_score":0.9999673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02477749570489554,"score_gpt":0.2483768843523043,"score_spread":0.2235993886474088,"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."}}