{"id":"W4400650325","doi":"10.1145/3678003","title":"A Knowledge Graph Embedding Model for Answering Factoid Entity Questions","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Topic Modeling","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Question answering; Computer science; Embedding; Knowledge graph; Information retrieval; Graph; Artificial intelligence; Natural language processing; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003582131,0.0001467811,0.0001401861,0.0005171782,0.0003385305,0.000814836,0.0005267646,0.00009136316,0.000002897717],"category_scores_gemma":[0.00001912076,0.0001451138,0.0001386085,0.0004406696,0.00001350026,0.003893112,0.00001306957,0.0001778155,0.0001140258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001375531,"about_ca_system_score_gemma":0.0001022145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003391675,"about_ca_topic_score_gemma":0.000006732785,"domain_scores_codex":[0.9988507,0.00002588758,0.0004792798,0.0002101338,0.0002070796,0.0002268919],"domain_scores_gemma":[0.9989539,0.0001443206,0.00005943039,0.0006137089,0.0001489875,0.00007963143],"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":[0.000002808842,0.00002061918,0.000001324678,0.0003094781,0.00003543616,2.434522e-7,0.006492646,0.8569275,0.00009335052,0.07337823,0.0001777804,0.06256057],"study_design_scores_gemma":[0.0001431372,0.00002320967,0.000002227286,0.0001610477,0.000008821325,0.00001172518,0.0001781528,0.9820682,0.0001582854,0.001387859,0.01570522,0.0001521597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00056185,0.0002394638,0.994743,0.000187829,0.002134617,0.0004929039,0.00005806276,0.0007946299,0.0007876737],"genre_scores_gemma":[0.9469703,0.0000404708,0.05217801,0.00003956386,0.00004823208,0.0003573511,0.00001085799,0.00001002483,0.0003451675],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9464085,"threshold_uncertainty_score":0.7857482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03891582147792144,"score_gpt":0.3021522972960036,"score_spread":0.2632364758180821,"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."}}