{"id":"W2169250641","doi":"10.1186/2041-1480-2-s2-s10","title":"Integration and publication of heterogeneous text-mined relationships on the Semantic Web","year":2011,"lang":"en","type":"article","venue":"Journal of Biomedical Semantics","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"U.S. National Library of Medicine; National Human Genome Research Institute; National Institutes of Health; National Science Foundation","keywords":"Ontology; Computer science; WordNet; Semantics (computer science); SPARQL; Semantic Web; Information retrieval; Syntax; Upper ontology; RDF; Natural language processing; World Wide Web; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0007847418,0.00009141787,0.0001620948,0.00009841454,0.0000638347,0.00001258464,0.000191193,0.0001919596,0.00002894738],"category_scores_gemma":[0.001676398,0.00005228157,0.00007513774,0.0001425661,0.0003802131,0.000004172123,0.00004710502,0.0002101371,0.000002688212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000747129,"about_ca_system_score_gemma":0.00006051082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002892643,"about_ca_topic_score_gemma":0.000005423112,"domain_scores_codex":[0.9989539,0.0001228287,0.0004366174,0.0001079381,0.0002630148,0.0001156902],"domain_scores_gemma":[0.9990939,0.00008244355,0.0003561304,0.0001714714,0.0001978243,0.0000981713],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007229669,0.001569342,0.01922764,0.0001651401,0.0006539587,0.00004761722,0.001949729,0.000002292087,0.7868884,0.005621956,0.04262897,0.140522],"study_design_scores_gemma":[0.006010945,0.02079251,0.2185739,0.001382787,0.0008630542,0.002538275,0.003750673,0.00687466,0.52469,0.01136126,0.2018073,0.001354606],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887195,0.0003884307,0.005947683,0.004367577,0.0002204454,0.00007488298,0.000006232805,0.000005625728,0.0002695703],"genre_scores_gemma":[0.9971284,0.0002705922,0.002167077,0.0001980966,0.0001383567,0.000001388217,0.00001045618,0.000007295744,0.00007838862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2621984,"threshold_uncertainty_score":0.2131981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04875573678008654,"score_gpt":0.2608897797600274,"score_spread":0.2121340429799409,"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."}}