{"id":"W4385570097","doi":"10.18653/v1/2023.findings-acl.882","title":"Learning Query Adaptive Anchor Representation for Inductive Relation Prediction","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Fundamental Research Funds for the Central Universities; Central China Normal University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Relation (database); Representation (politics); Task (project management); Relationship extraction; Feature learning; Graph; Feature (linguistics); Artificial intelligence; Machine learning; Data mining; Theoretical computer science","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":[0.0007074188,0.001302415,0.001116822,0.002400998,0.0004770258,0.0008547307,0.002621561,0.001340889,0.002661003],"category_scores_gemma":[0.004020668,0.0003772258,0.001124275,0.003050453,0.0007161969,0.004561259,0.001553604,0.001623095,0.001420022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009671896,"about_ca_system_score_gemma":0.0008996512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006992671,"about_ca_topic_score_gemma":0.009708105,"domain_scores_codex":[0.9990521,0.0001556517,0.00005000743,0.0004222835,0.000223353,0.00009656768],"domain_scores_gemma":[0.9985662,0.000646717,0.0001730535,0.0003550375,0.0001904197,0.00006848529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004659873,0.0005408631,0.00843947,0.0003615647,0.0001420944,0.0006853935,0.0004980656,0.2604418,0.01373752,0.02841464,0.02943099,0.6568415],"study_design_scores_gemma":[0.00001437624,0.00005014248,0.0007314696,0.00001764854,0.00003638436,0.00009099749,0.00007366214,0.9637668,0.003288245,0.02935654,0.002559946,0.00001386161],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05872916,0.001130326,0.9279803,0.0005958122,0.00008382738,0.000146129,0.002385876,0.006877616,0.00207091],"genre_scores_gemma":[0.7451937,0.0009090453,0.2381281,0.0004148116,0.0001300651,0.0001968693,0.01127789,0.0002641837,0.003485279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006992671,"threshold_uncertainty_score":0.01390392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04635087952193891,"score_gpt":0.2962809972788805,"score_spread":0.2499301177569416,"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."}}