{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002802892,0.0003659479,0.0003567024,0.0004487984,0.0002826348,0.0001616342,0.001988527,0.0003157776,0.000002876026],"category_scores_gemma":[0.00005833482,0.0004451234,0.0003679027,0.001221419,0.00009206612,0.0008395622,0.002102282,0.0005138421,0.00005245873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001990833,"about_ca_system_score_gemma":0.0001894052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004870883,"about_ca_topic_score_gemma":0.00008084513,"domain_scores_codex":[0.9976142,0.0000904979,0.0002307642,0.001466654,0.00008854917,0.0005093437],"domain_scores_gemma":[0.9977763,0.0002423811,0.0002774549,0.001235413,0.0003010131,0.0001673893],"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":[0.0000137479,0.00002087475,0.00004359789,0.00004470843,0.00003794494,0.00002381088,0.0001311798,0.5581526,0.000003436972,0.4383731,0.0003072371,0.002847772],"study_design_scores_gemma":[0.0002082416,0.00002573322,0.00008660249,0.00007962644,0.00002544934,8.067277e-7,0.00001588098,0.5568718,0.00002056951,0.4421761,0.0002249572,0.0002642466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009277843,0.00007956378,0.9858111,0.0001017036,0.00171606,0.0006069309,0.00001354751,0.0009401182,0.001453149],"genre_scores_gemma":[0.9659609,0.0003198201,0.03077635,0.00005177299,0.0001747104,0.00001069314,0.00006428,0.00005181376,0.002589664],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.956683,"threshold_uncertainty_score":0.9998,"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."}}