{"id":"W4224278993","doi":"10.3390/info13040205","title":"Medical Knowledge Graph Completion Based on Word Embeddings","year":2022,"lang":"en","type":"article","venue":"Information","topic":"Topic Modeling","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Beijing Municipality","keywords":"Word2vec; Computer science; RDF; Knowledge graph; Terminology; Information retrieval; Word (group theory); Natural language processing; Semantics (computer science); Graph; Artificial intelligence; Theoretical computer science; Semantic Web; Embedding; 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":[0.0005365827,0.0000571651,0.00006053205,0.0002003035,0.0002108178,0.00007053388,0.0004633166,0.0000261413,0.0002204933],"category_scores_gemma":[0.00005351728,0.00005839122,0.00003320282,0.0003109485,0.000009757772,0.0007216372,0.0001660095,0.0001679621,0.0001598895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008790768,"about_ca_system_score_gemma":0.00008027781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001135617,"about_ca_topic_score_gemma":0.000001507614,"domain_scores_codex":[0.9989654,0.00004778391,0.0002173663,0.00008521599,0.0005641587,0.0001200653],"domain_scores_gemma":[0.9995379,0.00005338226,0.0000745107,0.0002373695,0.00004252533,0.0000543018],"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":[0.00002309805,0.0001138645,0.0003154854,0.00004311334,0.000006291994,0.000003469111,0.006314973,0.09337178,0.0000140871,0.2140886,0.01452048,0.6711847],"study_design_scores_gemma":[0.0002499801,0.00003771894,0.000470324,0.000008445751,6.608273e-7,0.000004504984,0.00003834726,0.9372166,0.00002162513,0.0008567639,0.06102989,0.0000651205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01168531,0.000005281755,0.9702226,0.001565741,0.0005716864,0.0001013866,0.000001828349,0.0001837593,0.01566243],"genre_scores_gemma":[0.9913518,5.600992e-7,0.006201049,0.002321016,0.00003349678,0.00003632663,0.00002797047,0.000002055715,0.00002573594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9796665,"threshold_uncertainty_score":0.2414247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0161672622546258,"score_gpt":0.2530601875034533,"score_spread":0.2368929252488275,"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."}}