{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005763983,0.001265867,0.0006538096,0.002910213,0.0003847723,0.0008352753,0.0009295829,0.0009386342,0.002755174],"category_scores_gemma":[0.00433446,0.0003553061,0.001216582,0.00208345,0.0007018205,0.003400412,0.001258515,0.001478209,0.001723386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007105244,"about_ca_system_score_gemma":0.001175361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007678944,"about_ca_topic_score_gemma":0.01301681,"domain_scores_codex":[0.9991294,0.000139239,0.0000784381,0.0004399071,0.0001442576,0.00006875505],"domain_scores_gemma":[0.9986877,0.0005788027,0.0001773453,0.0002250435,0.0002682582,0.0000628549],"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":[0.0005350942,0.000297555,0.01569628,0.0009023241,0.0003139878,0.0009029443,0.0008840184,0.1162316,0.0156334,0.02606626,0.03081854,0.7917179],"study_design_scores_gemma":[0.00005248659,0.0001767155,0.003527046,0.0001344246,0.0001293757,0.0006251716,0.0004105095,0.9039583,0.009856975,0.06219626,0.01887449,0.00005822138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08384851,0.001773164,0.8957277,0.001174475,0.0002064601,0.0002903655,0.007781154,0.005925679,0.003272505],"genre_scores_gemma":[0.5900214,0.001444546,0.3609323,0.0006910027,0.0001948455,0.0004929561,0.04038642,0.0005295525,0.005306949],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007678944,"threshold_uncertainty_score":0.0152685,"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."}}