{"id":"W2510506392","doi":"","title":"On referring expressions in query answering over first order knowledge bases","year":2016,"lang":"en","type":"article","venue":"Principles of Knowledge Representation and Reasoning","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Conjunctive query; Context (archaeology); Noun phrase; Knowledge base; Description logic; Expression (computer science); Class (philosophy); Natural language processing; Artificial intelligence; Information retrieval; Relational database; Programming language; Noun","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":[0.0003358494,0.000164647,0.0002207691,0.0003369082,0.0001323025,0.0000583173,0.000396829,0.00008504291,0.00001601695],"category_scores_gemma":[0.001201526,0.0001142046,0.00004596525,0.000492789,0.00007305204,0.0005980359,0.0005078751,0.0001345985,0.000006300502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007556493,"about_ca_system_score_gemma":0.00007185231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005741134,"about_ca_topic_score_gemma":0.0001850655,"domain_scores_codex":[0.998696,0.00009841989,0.0003399961,0.0004703609,0.0001580954,0.0002371761],"domain_scores_gemma":[0.9984792,0.0007096282,0.0001587984,0.0004238608,0.0001412511,0.00008726599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001278903,0.0006756214,0.111483,0.0005832538,0.00005542896,0.00005800789,0.01269689,0.0001712349,0.09128206,0.4631997,0.0003996062,0.3192673],"study_design_scores_gemma":[0.00619386,0.0004370275,0.1682751,0.0307668,0.00004748943,0.0001284647,0.0007145567,0.1253359,0.6285196,0.02279933,0.0143444,0.002437413],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6095845,0.01052371,0.3535675,0.0006223749,0.000494833,0.0005276041,0.000005137616,0.0007561878,0.02391813],"genre_scores_gemma":[0.9385347,0.000209142,0.06044053,0.00001372852,0.00003976444,0.00003063936,0.00000114214,0.00001517475,0.000715112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5372375,"threshold_uncertainty_score":0.4657129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03158008085999161,"score_gpt":0.3247805165416627,"score_spread":0.2932004356816711,"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."}}