{"id":"W3119674270","doi":"10.1016/j.websem.2020.100625","title":"Knowledge graph embeddings for dealing with concept drift in machine learning","year":2021,"lang":"en","type":"article","venue":"Journal of Web Semantics","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Thales (Canada)","funders":"Engineering and Physical Sciences Research Council; Norges Forskningsråd; European Commission; Royal Society","keywords":"Computer science; Data stream mining; Knowledge extraction; Concept drift; Ontology; Artificial intelligence; Graph; Consistency (knowledge bases); Knowledge representation and reasoning; Machine learning; Natural language processing; 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":[],"consensus_categories":[],"category_scores_codex":[0.0005915012,0.0001231468,0.0002919841,0.0001910063,0.00006764594,0.0001440775,0.000560387,0.000054308,0.000004444983],"category_scores_gemma":[0.0002502119,0.0001033121,0.00008169041,0.0003301267,0.00003746147,0.0004002723,0.0001671081,0.0003621698,9.238721e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003125488,"about_ca_system_score_gemma":0.000199065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005428168,"about_ca_topic_score_gemma":0.00005546312,"domain_scores_codex":[0.9989377,0.00005760763,0.0004061136,0.0001747412,0.0002061125,0.0002177331],"domain_scores_gemma":[0.9987211,0.0002355292,0.0003659249,0.00022806,0.0003759648,0.0000734493],"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":[0.0004124928,0.00227545,0.1920872,0.0009918455,0.0009974114,0.007094043,0.03438313,0.008804987,0.08026552,0.3989175,0.02091099,0.2528594],"study_design_scores_gemma":[0.007992025,0.003872893,0.003489421,0.004549803,0.0002387345,0.005297318,0.001251685,0.6548405,0.1637286,0.01756012,0.135559,0.001619877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1044525,0.001482556,0.8919466,0.001123402,0.0002527197,0.000101108,0.000006568548,0.00008696887,0.0005475214],"genre_scores_gemma":[0.6267081,0.0001455038,0.3729491,0.00005128895,0.00005971315,0.000001077692,0.000002596718,0.00001282849,0.00006975722],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6460355,"threshold_uncertainty_score":0.4212946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01665660623354068,"score_gpt":0.2819623193054773,"score_spread":0.2653057130719366,"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."}}