{"id":"W4409572435","doi":"10.1016/j.eswa.2025.127612","title":"Fact retrieval from knowledge graphs through semantic and contextual attention","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University; Dalhousie University; Cape Breton University","funders":"Faculty of Graduate Studies and Research, University of Alberta; Natural Sciences and Engineering Research Council of Canada; Southern Methodist University; Saint Mary’s University","keywords":"Computer science; Knowledge graph; Semantic memory; Information retrieval; Natural language processing; Artificial intelligence; Cognition; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009439155,0.00147254,0.001047641,0.006742939,0.0008340465,0.001915725,0.001511363,0.001009583,0.002859103],"category_scores_gemma":[0.00624625,0.0004734283,0.001180189,0.004731975,0.0007187229,0.005462375,0.002624617,0.001159254,0.001429925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001119919,"about_ca_system_score_gemma":0.001449669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0144943,"about_ca_topic_score_gemma":0.02908644,"domain_scores_codex":[0.9989182,0.0002208757,0.00007220273,0.0004013985,0.0002965644,0.00009092684],"domain_scores_gemma":[0.9978178,0.00111337,0.0001666203,0.0004850892,0.0003250842,0.00009200008],"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":[0.0005163273,0.0003066912,0.004182513,0.001086054,0.0002863875,0.0005895783,0.0010479,0.04067267,0.03339256,0.01774191,0.04630357,0.8538738],"study_design_scores_gemma":[0.0001653066,0.0004182441,0.009109776,0.0001813444,0.0005866439,0.00103301,0.001144643,0.7489187,0.04039369,0.1307901,0.06708404,0.0001745982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07371933,0.005220774,0.8909152,0.0008650264,0.000219171,0.0003442231,0.004498389,0.01765089,0.00656695],"genre_scores_gemma":[0.4511371,0.002414108,0.5218453,0.0005754375,0.0002555613,0.0002001525,0.01832988,0.0007473658,0.004495017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0144943,"threshold_uncertainty_score":0.02881986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02090410090579666,"score_gpt":0.2828838789322674,"score_spread":0.2619797780264708,"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."}}