{"id":"W4381489754","doi":"10.1145/3594778.3594877","title":"EAGER: Explainable Question Answering Using Knowledge Graphs","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; York University; University of Waterloo; Ontario Tech University","funders":"","keywords":"Question answering; Computer science; Knowledge graph; Modular design; Pipeline (software); Graph; Natural language; Artificial intelligence; Natural language processing; Information retrieval; Theoretical computer science; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0003697556,0.00007591311,0.00007901937,0.0001816031,0.000135989,0.0001068327,0.0003507956,0.00003608566,0.000009099518],"category_scores_gemma":[0.00002200234,0.00007413965,0.00003518215,0.0006984153,0.000009697781,0.0005031062,0.0002458258,0.00006340308,0.000146465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000367458,"about_ca_system_score_gemma":0.00003488564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001020344,"about_ca_topic_score_gemma":0.00001300905,"domain_scores_codex":[0.9991887,0.00003432525,0.0001340818,0.0002727387,0.0001115213,0.0002586517],"domain_scores_gemma":[0.9994941,0.00003303295,0.00002303523,0.000354457,0.00004159491,0.00005380681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001130196,0.00002771438,0.001086602,0.00004904307,0.00001013699,0.00004140503,0.002125457,0.02341347,0.009446091,0.9099663,0.0008470849,0.05298556],"study_design_scores_gemma":[0.00007306731,0.000008037848,0.0002569174,0.00002264982,0.000001301876,0.000006087657,0.00005090097,0.9850021,0.002064015,0.01091632,0.001493438,0.0001051823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1995003,0.00008497322,0.7944446,0.0001571938,0.0004806975,0.00005960511,8.977376e-8,0.0007956733,0.004476864],"genre_scores_gemma":[0.8947793,0.00001746686,0.1028308,0.00005388684,0.00006507858,0.000006732633,7.259017e-7,0.000008872611,0.002237096],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9615886,"threshold_uncertainty_score":0.3023327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0500733304563139,"score_gpt":0.3037788247930684,"score_spread":0.2537054943367545,"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."}}