{"id":"W2043417858","doi":"10.1109/wse.2011.6081817","title":"Contextualized semantic analysis of web services","year":2011,"lang":"en","type":"article","venue":"","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Center for Advanced Study, University of Illinois at Urbana-Champaign; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Web service; World Wide Web; Latent Dirichlet allocation; Social Semantic Web; Contextualization; Service (business); Web modeling; Data Web; Devices Profile for Web Services; Semantic Web Stack; WS-Policy; Information retrieval; Web standards; Mashup; Service discovery; Web Coverage Service; Web development; Topic model; Web mapping; Web application security; Web intelligence; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.0001709994,0.0001475393,0.0003654647,0.0005203065,0.00005098628,0.00003534532,0.001353447,0.00005291717,0.0005265556],"category_scores_gemma":[0.000001297872,0.000111259,0.0001953139,0.002266435,0.00002720323,0.0003024709,0.0002917175,0.00006090095,0.00004637897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004699348,"about_ca_system_score_gemma":0.00002630557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001818195,"about_ca_topic_score_gemma":0.003292254,"domain_scores_codex":[0.9987608,0.00006405741,0.0003310632,0.0003506747,0.0002616591,0.0002317802],"domain_scores_gemma":[0.9987406,0.00007045148,0.0001572899,0.0008024721,0.0001384028,0.00009080507],"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":[0.0001529497,0.001047625,0.1382666,0.0006261324,0.008806963,0.00006446506,0.1102493,0.0002221237,0.03119648,0.6791469,0.0001375713,0.0300829],"study_design_scores_gemma":[0.002459659,0.0004506094,0.2010353,0.0001242628,0.002565829,0.00001610805,0.004314492,0.7228088,0.04940706,0.009691478,0.00585748,0.001268913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8169122,0.0002352017,0.08641192,0.0002108231,0.0002612971,0.0001580231,0.000007056471,0.0003453244,0.09545809],"genre_scores_gemma":[0.984337,0.00001267527,0.01403218,0.00147742,0.00001404629,0.000004790472,0.000005915838,0.00000562199,0.0001103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7225868,"threshold_uncertainty_score":0.5765414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609470223278819,"score_gpt":0.2267971146697558,"score_spread":0.2107024124369676,"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."}}