{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007932411,0.0003192675,0.0004084824,0.0001726447,0.00009788998,0.0001746165,0.001662096,0.000834475,0.0006174791],"category_scores_gemma":[0.0002391064,0.0002849647,0.0001239663,0.00005909086,0.0001136019,0.00165295,0.0007258673,0.001032496,0.001216599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009149242,"about_ca_system_score_gemma":0.000337256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001100837,"about_ca_topic_score_gemma":0.000001793361,"domain_scores_codex":[0.9965724,0.00001242115,0.001286554,0.0001116842,0.001672775,0.0003441065],"domain_scores_gemma":[0.9979552,0.00008452161,0.0005193623,0.0008936476,0.0001684337,0.0003788032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008415524,0.00002240921,0.000004603703,0.0005946499,0.00008031762,0.00001270616,0.006120114,0.000009954848,2.48837e-7,0.7098766,0.0893716,0.1938984],"study_design_scores_gemma":[0.0003443262,0.00006519455,0.000001313182,0.0002900886,0.00002511759,0.00004873003,0.00002280476,0.2091115,0.00002433761,0.03641262,0.7531658,0.0004881575],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00001259289,0.00005872567,0.606522,0.0003108559,0.0005428115,0.0001518539,0.00001237017,0.0001856499,0.3922031],"genre_scores_gemma":[0.0149523,0.005291558,0.2903334,0.08522568,0.002403089,0.00004673893,0.002158056,0.000286779,0.5993024],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.673464,"threshold_uncertainty_score":0.9999602,"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."}}