{"id":"W4402124327","doi":"10.1109/access.2024.3453215","title":"A Comprehensive Evaluation of Neural SPARQL Query Generation From Natural Language Questions","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Topic Modeling","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; SPARQL; RDF query language; Natural language generation; Query language; Natural language; Information retrieval; Natural language processing; Web search query; Artificial intelligence; Natural language user interface; Web query classification; Search engine; Semantic Web; RDF","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":[0.0001699993,0.00007805997,0.00009274278,0.00008563203,0.00004079932,0.0002648414,0.0004434612,0.00003669996,0.00001594256],"category_scores_gemma":[0.00002581518,0.0000715423,0.00004126794,0.0002154783,0.00001529889,0.0009757293,0.00008669822,0.0001104182,0.00001149992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004937848,"about_ca_system_score_gemma":0.00008577381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009462642,"about_ca_topic_score_gemma":0.0001499192,"domain_scores_codex":[0.9989508,0.0001213642,0.0001786873,0.0002740606,0.0003755941,0.00009949102],"domain_scores_gemma":[0.9993652,0.00007505927,0.00004411622,0.000305183,0.0001848812,0.00002563308],"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":[0.000006324009,0.00006332021,0.0005713672,0.00007167019,0.0001071243,0.00004609247,0.006042595,0.1801787,0.2803931,0.01597187,0.003814782,0.512733],"study_design_scores_gemma":[0.00009793208,0.000005949073,0.002380145,0.00002748787,0.00002015293,0.000003256346,0.00001559373,0.9832246,0.01275943,0.001314126,0.00007631203,0.0000750101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7387339,0.003073564,0.2551616,0.0003977326,0.002316484,0.0001228005,0.000005884165,0.0001071049,0.0000810066],"genre_scores_gemma":[0.9945998,0.000006492559,0.00475907,0.0001542363,0.0004092707,0.00001779153,0.00001818787,0.00000568411,0.00002945989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8030459,"threshold_uncertainty_score":0.291741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1106845422785603,"score_gpt":0.3852054805728277,"score_spread":0.2745209382942674,"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."}}