{"id":"W3032677642","doi":"","title":"Ontology Matching Using Convolutional Neural Networks","year":2020,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Convolutional neural network; Ontology; Matching (statistics); Artificial intelligence; Ontology alignment; Natural language processing; Information retrieval; Upper ontology; Semantic 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008545678,0.0007274238,0.0009152021,0.002933293,0.0007170082,0.001899626,0.001435287,0.001363382,0.004947209],"category_scores_gemma":[0.003316395,0.0005556102,0.001367668,0.002508183,0.0003849891,0.002982589,0.001720297,0.001215672,0.002001004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001646903,"about_ca_system_score_gemma":0.001967767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02932886,"about_ca_topic_score_gemma":0.04015288,"domain_scores_codex":[0.9991518,0.00008286892,0.00007136475,0.0003185895,0.0002241029,0.000151172],"domain_scores_gemma":[0.999034,0.0003118298,0.00009252374,0.0002598019,0.0002477278,0.00005407315],"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":[0.0004913381,0.0004100243,0.005219164,0.0002743041,0.0003329367,0.0003055409,0.000115556,0.07437024,0.01518306,0.01956409,0.01777181,0.8659618],"study_design_scores_gemma":[0.00002333099,0.0000367776,0.001439505,0.00005338632,0.00009513066,0.0001067093,0.00007313891,0.9432585,0.01166217,0.03592318,0.007307927,0.00002023211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09720037,0.002253724,0.876696,0.0007788156,0.0004010422,0.0002353326,0.002295966,0.009067803,0.01107088],"genre_scores_gemma":[0.6819758,0.001315314,0.2895972,0.0005107433,0.0001140439,0.000114713,0.009909254,0.0005305149,0.01593251],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02932886,"threshold_uncertainty_score":0.05831629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02659557028693389,"score_gpt":0.234270934172269,"score_spread":0.2076753638853351,"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."}}