{"id":"W2785761199","doi":"10.48550/arxiv.1802.04868","title":"SimplE Embedding for Link Prediction in Knowledge Graphs","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":395,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Simple (philosophy); Embedding; Computer science; Theoretical computer science; Link (geometry); Factorization; Graph embedding; Tying; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002081968,0.0002084487,0.0002726377,0.0003045065,0.0001451369,0.00003274114,0.0003468344,0.0003048707,0.000111831],"category_scores_gemma":[0.00006025322,0.0002560662,0.0002085335,0.000326481,0.00007519213,0.00008213268,0.000255738,0.0003048415,0.00007583068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001769123,"about_ca_system_score_gemma":0.00006749854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001595143,"about_ca_topic_score_gemma":0.00009819836,"domain_scores_codex":[0.9988334,0.00005736435,0.0002512748,0.0005886856,0.00003621653,0.0002330912],"domain_scores_gemma":[0.9987154,0.0002580383,0.0001909362,0.0005578882,0.0001875047,0.00009023834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006528467,0.000367848,0.002546031,0.0003781336,0.00009666773,0.000008536099,0.0004787626,0.005790415,0.000125149,0.974498,0.01540658,0.0002385973],"study_design_scores_gemma":[0.0005741592,0.00003215964,0.0007693234,0.0001019451,0.00009625556,0.000001080559,0.00009786253,0.2020524,0.0001161088,0.7926654,0.003273191,0.0002200942],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.807194,0.00002928574,0.1847373,0.00007755363,0.000354826,0.001193044,0.0003109114,0.0003588774,0.005744167],"genre_scores_gemma":[0.9951166,0.00003728747,0.002563295,0.00002157155,0.0001883734,0.00001629997,0.0001434099,0.0000338228,0.001879376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.196262,"threshold_uncertainty_score":0.9999892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1375780814057204,"score_gpt":0.2808541760111277,"score_spread":0.1432760946054072,"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."}}