{"id":"W3127274234","doi":"10.18653/v1/2021.eacl-main.245","title":"Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Samsung; Canadian Institute for Advanced Research; Microsoft Research","keywords":"Shot (pellet); Computer science; Relation (database); Link (geometry); Simple (philosophy); Set (abstract data type); Artificial intelligence; One shot; Machine learning; Training set; Baseline (sea); Algorithm; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005480007,0.00138941,0.001590447,0.002076381,0.001147063,0.002574248,0.003331662,0.002870445,0.001597231],"category_scores_gemma":[0.02818829,0.0008933682,0.0008374698,0.001620511,0.002435053,0.01005455,0.002424262,0.004078762,0.0005974652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001709883,"about_ca_system_score_gemma":0.0008451025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007600088,"about_ca_topic_score_gemma":0.009674638,"domain_scores_codex":[0.9974215,0.001014601,0.00008838433,0.001041855,0.0002822255,0.0001514348],"domain_scores_gemma":[0.967819,0.02763365,0.001070179,0.002284119,0.000629581,0.0005633789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006039424,0.0004386201,0.01451552,0.0007399633,0.0003058045,0.0003504784,0.0007776644,0.781575,0.002194956,0.03502446,0.009103415,0.1543701],"study_design_scores_gemma":[0.00001177004,0.00004860832,0.0006632631,0.0000404272,0.00002285372,0.00007348847,0.0000615587,0.9426178,0.0005782331,0.05525007,0.0006178685,0.00001398905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4077415,0.007984458,0.5683019,0.003851058,0.0002057127,0.0001595414,0.001809512,0.002893189,0.007053087],"genre_scores_gemma":[0.9527749,0.001065449,0.04131162,0.0006291349,0.0001586669,0.00007232557,0.002073196,0.0002191583,0.001695509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007600088,"threshold_uncertainty_score":0.02898139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1633285299933661,"score_gpt":0.3043762447346737,"score_spread":0.1410477147413077,"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."}}