{"id":"W2954222332","doi":"10.1016/j.knosys.2019.104925","title":"Context-aware instance matching through graph embedding in lexical semantic space","year":2019,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Topic Modeling","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario College of Art and Design; Université du Québec à Chicoutimi; Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Canada Foundation for Innovation; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Computer science; Ontology alignment; Knowledge graph; Embedding; Information retrieval; RDF; Semantic Web; Entity linking; Matching (statistics); Knowledge base; Referent; Theoretical computer science; Artificial intelligence; Ontology-based data integration","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007367644,0.0002980192,0.0005308152,0.0002319897,0.0001142946,0.0002641974,0.001019462,0.0001675051,0.000008799625],"category_scores_gemma":[0.00002470768,0.0002877403,0.0001289657,0.0007068515,0.00003824169,0.0006329505,0.0001810969,0.000385937,0.0004178481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002219478,"about_ca_system_score_gemma":0.0001901243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003852113,"about_ca_topic_score_gemma":0.0001874306,"domain_scores_codex":[0.9973203,0.0003034901,0.0005980975,0.0008175342,0.0003702047,0.0005903891],"domain_scores_gemma":[0.998257,0.0003052147,0.0001719705,0.001040324,0.0001188152,0.0001066288],"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":[0.00006322188,0.0005656747,0.04424206,0.002924467,0.00008192934,0.0002214445,0.02400027,0.182729,0.005978387,0.7289107,0.001181299,0.009101542],"study_design_scores_gemma":[0.001601727,0.00006029533,0.000382496,0.001807869,0.000004678074,0.00002091247,0.0009134823,0.9863821,0.0009057294,0.002080077,0.005269811,0.0005708513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2050697,0.002412926,0.7856246,0.0002596919,0.002372037,0.000542926,0.000002087192,0.0002673265,0.003448736],"genre_scores_gemma":[0.9948658,0.000006733357,0.004287747,0.0001205549,0.0001464123,0.00004646118,0.000002570863,0.00002973101,0.0004939924],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8036531,"threshold_uncertainty_score":0.9999575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02465723400931598,"score_gpt":0.2784244017600168,"score_spread":0.2537671677507009,"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."}}