{"id":"W296396633","doi":"10.1007/978-3-319-08852-5_41","title":"Learning Categories from Linked Open Data","year":2014,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"RDF; Computer science; Linked data; Information retrieval; Process (computing); Extensional definition; Subject (documents); Representation (politics); Resource (disambiguation); Metadata; Hierarchy; Similarity (geometry); Web resource; World Wide Web; Data science; Semantic Web; Artificial intelligence","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":[0.001960149,0.000684086,0.0007759331,0.004347519,0.0008760355,0.003439905,0.002000011,0.001103173,0.00575854],"category_scores_gemma":[0.01209975,0.0005036751,0.001372631,0.004765356,0.001487595,0.01047765,0.004678677,0.00285147,0.002941137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001207987,"about_ca_system_score_gemma":0.001149392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002087873,"about_ca_topic_score_gemma":0.003416202,"domain_scores_codex":[0.9982854,0.0004480011,0.0001477307,0.0003377589,0.0007195815,0.00006158897],"domain_scores_gemma":[0.9959946,0.002816211,0.0001030177,0.0005953929,0.0004099208,0.0000808935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003371037,0.00006488965,0.001226532,0.0004108362,0.00006345948,0.00007672866,0.0005900842,0.007400492,0.0007286168,0.1577452,0.02067255,0.8109869],"study_design_scores_gemma":[0.000007842641,0.00001807812,0.000579066,0.0002434598,0.00002746639,0.0001365955,0.000499981,0.07461657,0.001638462,0.8592754,0.06293222,0.00002483354],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01241883,0.003324669,0.9641899,0.00187618,0.0003289003,0.0001323635,0.001318017,0.00210821,0.01430281],"genre_scores_gemma":[0.1727557,0.006116427,0.7858316,0.0005983187,0.0004197215,0.0005182428,0.01359946,0.0006173818,0.0195432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00575854,"threshold_uncertainty_score":0.01926428,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1127226161547305,"score_gpt":0.3343144556611146,"score_spread":0.2215918395063841,"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."}}