{"id":"W3080269789","doi":"10.24963/kr.2020/87","title":"Ontology-guided Semantic Composition for Zero-shot Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thales (Canada)","funders":"Samsung; Norges Forskningsråd; Siemens; Engineering and Physical Sciences Research Council; University of Oxford","keywords":"Ontology; Computer science; Embedding; Semantics (computer science); Class (philosophy); OWL-S; Natural language processing; Information retrieval; Artificial intelligence; Shot (pellet); Ontology learning; Semantic Web; Web Ontology Language; Zero (linguistics); Upper ontology; Semantic Web Stack; Suggested Upper Merged Ontology; Programming language; 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":[0.002133006,0.0008709418,0.001345788,0.001780043,0.001064139,0.001134467,0.002798592,0.001288724,0.002660098],"category_scores_gemma":[0.004596832,0.0004914639,0.001365631,0.001292405,0.001760139,0.005214723,0.004348704,0.002241443,0.0007249242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001348815,"about_ca_system_score_gemma":0.001727073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004326596,"about_ca_topic_score_gemma":0.006478864,"domain_scores_codex":[0.9982529,0.0004186719,0.0001081749,0.000596956,0.0004668105,0.0001564266],"domain_scores_gemma":[0.9985312,0.0005952618,0.00009083833,0.0004159292,0.0002453276,0.0001213697],"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":[0.0003966828,0.0007620226,0.002847763,0.0004697643,0.0001770664,0.0002603011,0.0009191601,0.09491799,0.02668602,0.09193584,0.006064184,0.7745633],"study_design_scores_gemma":[0.00002149862,0.00007721711,0.0004019982,0.00002361716,0.00003737428,0.00009311915,0.000149959,0.8843849,0.008591872,0.1031426,0.003053175,0.00002276329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01242942,0.0001708958,0.9852642,0.0001101011,0.0000243783,0.00006491704,0.00009192064,0.001110288,0.0007339439],"genre_scores_gemma":[0.4821994,0.0003535759,0.5111238,0.000462815,0.00008494305,0.0002569225,0.002034487,0.0003574293,0.00312667],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004326596,"threshold_uncertainty_score":0.0112806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0803430304813903,"score_gpt":0.2987381748772592,"score_spread":0.2183951443958689,"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."}}