{"id":"W1633230169","doi":"10.1609/aimag.v35i1.2502","title":"Natural Language Access to Enterprise Data","year":2014,"lang":"en","type":"article","venue":"AI Magazine","topic":"Topic Modeling","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Siemens (Canada)","funders":"","keywords":"Computer science; Syntax; Data control language; Semantics (computer science); Natural language; Set (abstract data type); Data access; Enterprise data management; Question answering; Interpretation (philosophy); Query language; Data manipulation language; Programming language; Information retrieval; Enterprise information system; Database; World Wide Web; Data science; Artificial intelligence; Web search query; Query by Example","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002141361,0.00008201761,0.00009444328,0.00005865337,0.00003290045,0.0002423273,0.003079336,0.0000201196,0.00002664878],"category_scores_gemma":[0.0001099252,0.00007158401,0.00001583152,0.0001665107,0.00000739442,0.000857649,0.002368579,0.0001033379,0.0005280512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001177319,"about_ca_system_score_gemma":0.00001463406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003078809,"about_ca_topic_score_gemma":0.00005859264,"domain_scores_codex":[0.9990867,0.00002890628,0.0001199265,0.0003963551,0.0001707613,0.0001973924],"domain_scores_gemma":[0.9981192,0.00003637956,0.00002510735,0.001707981,0.0000286498,0.00008262623],"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":[0.000009285463,0.00005542406,0.002075033,0.0000281571,0.00001662765,0.00004182163,0.001172835,0.0003287185,0.00549585,0.00949163,0.1338453,0.8474393],"study_design_scores_gemma":[0.0002812097,0.00002821715,0.006719835,0.00002408964,0.000004162448,0.00001344177,0.000003853066,0.8492846,0.0005320843,0.0004944575,0.1423993,0.0002146823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01997129,0.0001034044,0.965965,0.009428404,0.0006873391,0.000098827,0.000003724589,0.0001809409,0.003561016],"genre_scores_gemma":[0.9476665,0.00000138879,0.04146424,0.009488204,0.0002242144,0.000003412413,0.00001319494,0.000006836203,0.001131997],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9276952,"threshold_uncertainty_score":0.6787207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02627650944316974,"score_gpt":0.3160102430337011,"score_spread":0.2897337335905314,"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."}}