{"id":"W4317042660","doi":"10.1145/3578708","title":"Fast and Accurate Framework for Ontology Matching in Web of Things","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Asian and Low-Resource Language Information Processing","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Cluster analysis; Ontology; Data mining; Information retrieval; Matching (statistics); Web of Things; Semantic Web; Interoperability; The Internet; Artificial intelligence; World Wide Web; Mathematics","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":[0.0002517594,0.0001040274,0.0001593209,0.0003629325,0.0001608031,0.0001635228,0.0002599095,0.0001006329,0.000003520765],"category_scores_gemma":[0.0001057515,0.00009350008,0.00002777587,0.0004122428,0.00005077499,0.001336505,0.00001936299,0.0001658499,0.000003636715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001141203,"about_ca_system_score_gemma":0.00004033058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002454499,"about_ca_topic_score_gemma":0.00003496189,"domain_scores_codex":[0.9992534,0.00002233651,0.000285131,0.0001341402,0.0001110505,0.0001939348],"domain_scores_gemma":[0.999404,0.000206825,0.0001272471,0.0001942689,0.00002842758,0.00003925938],"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":[0.00002198285,0.00001302113,0.00005538138,0.0002618306,0.000006438438,0.000002288992,0.09072652,0.0004781987,0.0001205965,0.002066212,0.000006498679,0.906241],"study_design_scores_gemma":[0.003519363,0.0003404832,0.01124212,0.002305562,0.00004772027,0.0001439638,0.2864012,0.6510721,0.006500038,0.03587526,0.001587214,0.000965006],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1325027,0.00009930104,0.8644888,0.001935247,0.00004589531,0.0001567084,0.000004542495,0.0001693108,0.000597454],"genre_scores_gemma":[0.9518368,0.0000228871,0.04746981,0.0006062727,0.000008059321,0.00002032982,0.000005000853,0.000004949999,0.00002587729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.905276,"threshold_uncertainty_score":0.3812822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01204690886698901,"score_gpt":0.2703067519801377,"score_spread":0.2582598431131487,"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."}}