{"id":"W4230743089","doi":"10.4018/978-1-60566-050-9.ch048","title":"Information Retrieval by Semantic Similarity","year":2011,"lang":"en","type":"book-chapter","venue":"Medical Informatics","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Semantic similarity; WordNet; Information retrieval; Computer science; Explicit semantic analysis; Ontology; Similarity (geometry); Semantic integration; Semantic computing; Vector space model; Semantic search; Natural language processing; Ontology-based data integration; Artificial intelligence; Semantic technology; Semantic Web","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":[0.004831193,0.00148165,0.002730692,0.01383419,0.001105811,0.007183242,0.002341524,0.002596537,0.01013129],"category_scores_gemma":[0.01534647,0.0006111821,0.001927339,0.01928472,0.002359323,0.01541529,0.004669832,0.001851071,0.008313918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002351147,"about_ca_system_score_gemma":0.001626009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001451456,"about_ca_topic_score_gemma":0.001108726,"domain_scores_codex":[0.9909503,0.003695457,0.0008152655,0.001085257,0.003195305,0.0002584637],"domain_scores_gemma":[0.9966261,0.001696769,0.0002397505,0.0008403769,0.0005280257,0.00006904006],"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":[0.0001464081,0.0002162462,0.001049649,0.002018632,0.0003475431,0.0002448589,0.0005220021,0.0120491,0.006846529,0.25633,0.04358114,0.6766478],"study_design_scores_gemma":[0.0001093559,0.0003098661,0.001428331,0.000641543,0.0002060098,0.001375518,0.0006508791,0.1504825,0.00974799,0.6438791,0.1909617,0.0002073266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008686733,0.02144913,0.9299759,0.002545645,0.0007796675,0.00107954,0.001387864,0.001911513,0.03218389],"genre_scores_gemma":[0.161556,0.02642167,0.7823173,0.00128221,0.001717504,0.001583257,0.005784774,0.0004034918,0.01893387],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01383419,"threshold_uncertainty_score":0.03389257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02606271963612654,"score_gpt":0.2341778444848152,"score_spread":0.2081151248486887,"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."}}