{"id":"W4385060307","doi":"10.1007/978-3-031-38333-5_9","title":"Guided Rotational Graph Embeddings for Error Detection in Noisy Knowledge Graphs","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Embedding; Computer science; Knowledge graph; Path (computing); Adversarial system; Ranking (information retrieval); Theoretical computer science; Graph; Artificial intelligence; Benchmark (surveying); Representation (politics); Machine learning; Algorithm","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":[0.001457924,0.001591292,0.001780932,0.001742073,0.000510838,0.001597726,0.002498878,0.00216682,0.004356496],"category_scores_gemma":[0.01340713,0.00075238,0.001073885,0.001828887,0.001216272,0.004220639,0.002818896,0.0028708,0.001783036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006841,"about_ca_system_score_gemma":0.0008643281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003199707,"about_ca_topic_score_gemma":0.003975495,"domain_scores_codex":[0.9983455,0.0004920579,0.0001010719,0.0005037634,0.0004316032,0.000126003],"domain_scores_gemma":[0.9938512,0.003720535,0.0004727622,0.001111334,0.0007090869,0.0001351463],"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":[0.0004151579,0.0001716035,0.000916409,0.0003987341,0.000118754,0.0001456595,0.000188797,0.3389904,0.006579753,0.04827862,0.01158951,0.5922066],"study_design_scores_gemma":[0.000007375054,0.00003471274,0.000136466,0.0000216724,0.00001214236,0.00004206203,0.00002692211,0.9582048,0.001743328,0.03890136,0.0008598466,0.000009321413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007221538,0.0003436948,0.9899271,0.0001722084,0.00008103067,0.00004149977,0.0001900336,0.001289162,0.0007337541],"genre_scores_gemma":[0.3323503,0.0008037265,0.6566439,0.0002686662,0.0002055103,0.0002010365,0.002131963,0.0009051678,0.006489801],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004356496,"threshold_uncertainty_score":0.01457393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03185377469005752,"score_gpt":0.2791791002831925,"score_spread":0.247325325593135,"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."}}