{"id":"W4414548709","doi":"10.1007/978-981-96-5604-2_22","title":"Interpreting and Visualizing SQL Queries from Natural Language: An NLP Architecture (SQLGenie)","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"SQL; Natural language; Query language; Usability; Natural language user interface; Data definition language; Query by Example; Natural (archaeology); Language Integrated Query","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002233293,0.001344065,0.0009422927,0.001709096,0.0007664973,0.006581757,0.00216009,0.001279679,0.01510976],"category_scores_gemma":[0.003029525,0.001412489,0.001289906,0.002077108,0.001697116,0.00471895,0.003132628,0.002143266,0.008701256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001078407,"about_ca_system_score_gemma":0.001495311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00618077,"about_ca_topic_score_gemma":0.007531096,"domain_scores_codex":[0.9990702,0.0001844409,0.000101109,0.0002409977,0.0003472563,0.00005595709],"domain_scores_gemma":[0.9989343,0.0005915399,0.00004124885,0.0001955714,0.0001732625,0.00006412657],"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":[0.0002912433,0.0002581985,0.001319157,0.0008621585,0.0001221515,0.0008313261,0.004885856,0.01115091,0.03373449,0.2197138,0.1736695,0.5531611],"study_design_scores_gemma":[0.00009434201,0.00007533518,0.0009880181,0.0003976213,0.0001318326,0.0009725134,0.0007667757,0.1594279,0.07506705,0.1681297,0.5938026,0.0001462911],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00250787,0.0003320655,0.936701,0.0004277968,0.00008277747,0.000163846,0.001252593,0.04872223,0.00980985],"genre_scores_gemma":[0.02315194,0.0009481976,0.9284569,0.0005725778,0.00006134465,0.0003111992,0.007009098,0.01323483,0.02625391],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01510976,"threshold_uncertainty_score":0.05054712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008326599949208298,"score_gpt":0.2487949926510604,"score_spread":0.2404683927018521,"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."}}