{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008246974,0.0008858813,0.0005881728,0.001824054,0.0004169195,0.001088507,0.001304488,0.001613952,0.003339591],"category_scores_gemma":[0.004701399,0.0003209591,0.001238884,0.001839459,0.0004443925,0.004218193,0.001087955,0.001679779,0.001570381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008946029,"about_ca_system_score_gemma":0.0009429133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0080407,"about_ca_topic_score_gemma":0.01417117,"domain_scores_codex":[0.9992527,0.0002147864,0.00005494115,0.0002719703,0.0001595227,0.00004608362],"domain_scores_gemma":[0.9987155,0.0007712581,0.00008948192,0.0001650016,0.0002157126,0.0000430006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003684964,0.0006017454,0.004557539,0.0009338883,0.000302885,0.0004862056,0.0009628263,0.2510891,0.009530568,0.07786281,0.04336503,0.6099389],"study_design_scores_gemma":[0.00002125692,0.00006433253,0.0006543196,0.00004270994,0.00005396104,0.0001578954,0.000130006,0.9210511,0.001444813,0.06502626,0.01132976,0.00002355459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0148398,0.001439142,0.9747021,0.001057939,0.0001124534,0.0002073453,0.00273062,0.001972531,0.002938038],"genre_scores_gemma":[0.3943295,0.002237746,0.5692882,0.0009820805,0.0002877446,0.0005936889,0.02113522,0.0002961592,0.01084961],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0080407,"threshold_uncertainty_score":0.01598781,"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."}}