{"id":"W4387559172","doi":"10.48550/arxiv.2310.04562","title":"Towards Foundation Models for Knowledge Graph Reasoning","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Samsung; Alliance de recherche numérique du Canada; Tencent; Canadian Institute for Advanced Research; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Microsoft Research","keywords":"Inference; Vocabulary; Computer science; Knowledge graph; Graph; Artificial intelligence; Natural language processing; Relation (database); Foundation (evidence); Language model; Question answering; Theoretical computer science; Data mining; Linguistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001945996,0.001060641,0.0008927559,0.001789607,0.0006862849,0.002171334,0.002875117,0.001616035,0.004148264],"category_scores_gemma":[0.009989048,0.0009626283,0.002285093,0.00132717,0.001838404,0.007545137,0.003351092,0.004643913,0.001382927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002019711,"about_ca_system_score_gemma":0.001738262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00697596,"about_ca_topic_score_gemma":0.01514155,"domain_scores_codex":[0.9986832,0.0004532853,0.00007806107,0.000412354,0.0002798873,0.00009325133],"domain_scores_gemma":[0.9959826,0.002417834,0.0002043362,0.0009030977,0.000356027,0.0001359963],"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":[0.0001729909,0.0001755327,0.002369097,0.0005373864,0.0002330366,0.0002187937,0.0005186776,0.4384751,0.003858801,0.3055213,0.01699153,0.2309277],"study_design_scores_gemma":[0.0000111927,0.00001495132,0.0001331189,0.00003947903,0.00001748103,0.00002462081,0.0000402674,0.6946651,0.0008092108,0.301481,0.002755287,0.000008336397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01332388,0.0003862345,0.979183,0.0009182387,0.00004432179,0.00006176771,0.0008103347,0.003124521,0.002147709],"genre_scores_gemma":[0.3956923,0.001008065,0.5905675,0.00103448,0.0001389591,0.0003297765,0.006110714,0.0008127596,0.004305437],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00697596,"threshold_uncertainty_score":0.01465404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1486863360974331,"score_gpt":0.2355385314867481,"score_spread":0.08685219538931502,"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."}}