{"id":"W4229012760","doi":"10.1007/s10489-022-03235-7","title":"Hashing-based semantic relevance attributed knowledge graph embedding enhancement for deep probabilistic recommendation","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"Novelis (Canada)","funders":"Basic Research Program of Jiangsu Province; Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Hash function; Theoretical computer science; Graph; Probabilistic logic; Embedding; Data mining; Information retrieval; Artificial intelligence","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007662986,0.0003122392,0.0003058655,0.0002098725,0.0008401183,0.0001155937,0.001404639,0.00005573382,0.00008484877],"category_scores_gemma":[0.0001085442,0.000345793,0.0001283234,0.001545541,0.0000853223,0.0002813202,0.0004932397,0.000401947,0.00003473144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002847199,"about_ca_system_score_gemma":0.00006920251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002760533,"about_ca_topic_score_gemma":0.00001535277,"domain_scores_codex":[0.9972958,0.0001080502,0.0005943397,0.001019754,0.0003165644,0.0006654941],"domain_scores_gemma":[0.9975982,0.001084128,0.0003122441,0.0007347852,0.0001538799,0.0001167976],"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":[0.000103369,0.0003712288,0.00001778829,0.0001004372,0.00002923593,0.000003244558,0.0006166803,0.4228531,0.003585705,0.1474058,0.001272493,0.4236409],"study_design_scores_gemma":[0.0002687248,0.0002677597,0.00001029978,0.00002628678,0.00001269779,0.000005062197,0.00007272608,0.8787454,0.01966062,0.07740828,0.0230493,0.0004727989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009616594,0.00027895,0.9952278,0.0005388937,0.0008356482,0.001518342,0.000009620875,0.0003381182,0.0002910385],"genre_scores_gemma":[0.8444124,0.00004220788,0.1526018,0.0006619109,0.00005076775,0.002058293,0.00009045733,0.00003388118,0.00004826088],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8434508,"threshold_uncertainty_score":0.9998994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03186132315158871,"score_gpt":0.298597127564047,"score_spread":0.2667358044124584,"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."}}