{"id":"W2403558166","doi":"","title":"Using Ontology Alignment for the TAC RTE Challenge.","year":2008,"lang":"en","type":"article","venue":"","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Ontology; Task (project management); Textual entailment; Logical consequence; Natural language processing; Ontology alignment; Artificial intelligence; Fragment (logic); Information retrieval; Upper ontology; Semantic Web; Programming language","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":[0.0001464214,0.00007680986,0.00008508646,0.00003037614,0.000228185,0.0000298117,0.0007253595,0.00004617076,0.000007923814],"category_scores_gemma":[0.00002477034,0.00004509675,0.00004135441,0.00008639848,0.00004841844,0.0001703019,0.0001643286,0.00006309607,0.000003399921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000343926,"about_ca_system_score_gemma":0.00003883519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006162166,"about_ca_topic_score_gemma":0.00001802636,"domain_scores_codex":[0.9993712,0.00001781537,0.0001031854,0.0001988888,0.0001172251,0.0001916496],"domain_scores_gemma":[0.9994174,0.0001132579,0.00004426718,0.0003491264,0.00005055738,0.00002536659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009706649,0.00007847673,0.00005686101,0.0000222138,0.00003818144,0.00004027293,0.002084653,0.00002291972,0.004475902,0.8796852,0.007488647,0.105997],"study_design_scores_gemma":[0.0009323796,0.0004593625,0.0001381954,0.00005231837,0.00003363549,0.0008627954,0.0001397137,0.4765944,0.1419522,0.3074529,0.0705259,0.0008562381],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003882974,0.003371438,0.9902955,0.004481628,0.0001573619,0.0001954318,3.417102e-7,0.0003251639,0.0007848542],"genre_scores_gemma":[0.2743632,0.00004747994,0.7243203,0.0008263437,0.00005429213,0.00002261525,2.090922e-7,0.000004575657,0.0003609676],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5722324,"threshold_uncertainty_score":0.1838992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08163474028519087,"score_gpt":0.3294883282792935,"score_spread":0.2478535879941027,"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."}}