{"id":"W4393842226","doi":"10.5281/zenodo.7916715","title":"Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Benchmark (surveying); Ontology; Computer science; Knowledge graph; Graph; Information retrieval; Artificial intelligence; Natural language processing; Theoretical computer science; Philosophy; Geography; Cartography; Epistemology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003620449,0.003729399,0.001029707,0.006902475,0.00124892,0.003308079,0.005048551,0.003106967,0.04371759],"category_scores_gemma":[0.03419358,0.001039844,0.002561169,0.005989856,0.0009526186,0.004927371,0.003731323,0.002258434,0.02325145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002716003,"about_ca_system_score_gemma":0.003200712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01902744,"about_ca_topic_score_gemma":0.02274307,"domain_scores_codex":[0.9948971,0.001493494,0.0006357568,0.001077843,0.001627781,0.0002681042],"domain_scores_gemma":[0.9828279,0.01154113,0.0005302949,0.002069896,0.002501541,0.0005293236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005928436,0.0005569287,0.001182923,0.006559509,0.0003424284,0.0007881703,0.0005777735,0.02396039,0.0057976,0.009856639,0.7644423,0.1853425],"study_design_scores_gemma":[0.001841977,0.000561404,0.003014822,0.001212756,0.0002482693,0.001025057,0.001508153,0.3428118,0.03286556,0.04930655,0.5653668,0.0002369292],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02398638,0.00325603,0.1789748,0.00352134,0.001691022,0.003138408,0.4056271,0.3371593,0.04264558],"genre_scores_gemma":[0.02986537,0.0008594404,0.203278,0.0008083996,0.00008413713,0.001703692,0.7412088,0.01565454,0.006537708],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04371759,"threshold_uncertainty_score":0.1462499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07387188460432378,"score_gpt":0.28338009468257,"score_spread":0.2095082100782462,"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."}}