{"id":"W1517555792","doi":"10.1007/978-3-540-30182-0_48","title":"Query Builder: A Natural Language Interface for Structured Databases","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Parsing; Natural language user interface; Interface (matter); Feature (linguistics); Programming language; Natural language; Natural language processing; User interface; Artificial intelligence; Natural (archaeology); Human–computer interaction; Information retrieval; Database; Operating system; Linguistics","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":[0.003874036,0.001943285,0.002030401,0.003110911,0.000883051,0.003992149,0.003558838,0.002001474,0.03773881],"category_scores_gemma":[0.009819335,0.001814765,0.001539647,0.002785829,0.001165278,0.008041228,0.004894468,0.002370469,0.01533636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00121131,"about_ca_system_score_gemma":0.001554186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004588032,"about_ca_topic_score_gemma":0.005126878,"domain_scores_codex":[0.997822,0.0004344455,0.0003690521,0.0003420755,0.0009115039,0.0001208743],"domain_scores_gemma":[0.9964818,0.002255089,0.000155203,0.0005069835,0.0004160497,0.0001848623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001375022,0.0002748197,0.001790368,0.002925616,0.0002815986,0.0009182264,0.002858399,0.005721551,0.03755773,0.08682672,0.5253028,0.3341671],"study_design_scores_gemma":[0.0007671124,0.0002125963,0.001461007,0.0006231874,0.0002936331,0.001597588,0.0006280557,0.1181445,0.05494564,0.1020596,0.7188972,0.0003697812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001872463,0.0003730601,0.7947884,0.0003885678,0.00008179797,0.0003679533,0.008408542,0.1899072,0.003812119],"genre_scores_gemma":[0.05131075,0.001479619,0.8179696,0.002214221,0.0001831503,0.002005312,0.04920239,0.05265968,0.02297533],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03773881,"threshold_uncertainty_score":0.1262488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01952414774588913,"score_gpt":0.283760073659325,"score_spread":0.2642359259134359,"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."}}