{"id":"W4403736865","doi":"10.18280/isi.290518","title":"Expanding the User’s Query to Enhance Semantic Information Retrieval Using the Reasoning Mechanism Based on Homomorphism Between Semantic Annotations","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Information retrieval; Mechanism (biology); Semantic query; Homomorphism; Semantic analytics; Semantic computing; Semantic Web; World Wide Web; Web search query; Search engine; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001692992,0.0002537678,0.0002035092,0.0004740607,0.001258106,0.002223992,0.0006748499,0.0001032166,0.000004900886],"category_scores_gemma":[0.000533312,0.000179014,0.0001108248,0.001823406,0.00007770133,0.004077415,0.0002215044,0.000393527,0.0001481565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003329287,"about_ca_system_score_gemma":0.0002037287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007129163,"about_ca_topic_score_gemma":0.000004608484,"domain_scores_codex":[0.997889,0.0002035174,0.0006808838,0.0002131582,0.000582482,0.0004309114],"domain_scores_gemma":[0.997946,0.0008408807,0.0002686501,0.0005166699,0.0003391439,0.00008860449],"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":[0.0001406361,0.0000582846,0.0009560736,0.001391038,0.0003346125,0.00002931126,0.1289224,0.2367778,0.001749252,0.1148247,0.003742839,0.511073],"study_design_scores_gemma":[0.0001210632,0.0001084151,0.001205211,0.001679296,0.00004343785,0.00003112318,0.0007508048,0.985617,0.005884118,0.003164195,0.001092059,0.0003032536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1835628,0.00004923241,0.8133869,0.0007281427,0.0008423687,0.0005296072,0.00001174591,0.0003855642,0.0005036001],"genre_scores_gemma":[0.9929158,0.000007448206,0.005639085,0.001127378,0.00021151,0.00002220234,0.00004511048,0.00001306086,0.00001840002],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.809353,"threshold_uncertainty_score":0.9988118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0195222846178164,"score_gpt":0.2752576971525962,"score_spread":0.2557354125347798,"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."}}