About https://w3id.org/scholarlydata/inproceedings/iswc-2019-workshop-30
Subject: https://w3id.org/scholarlydata/inproceedings/iswc-2019-workshop-30
Property: http://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.w3.org/2002/07/owl#Thinghttp://purl.org/spar/fabio/ProceedingsPaperhttps://w3id.org/scholarlydata/ontology/conference-ontology.owl#InProceedingshttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#SocialObjecthttp://www.w3.org/2000/01/rdf-schema#Resourcehttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#InformationObjecthttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#ObjectProperty: http://www.w3.org/2000/01/rdf-schema#label7th International Workshop on Semantic Statistics (SemStats 2019)Property: http://swrc.ontoware.org/ontology#abstractThis workshop aims to explore and strengthen the relationship between the Semantic Web and statistical communities, to provide better access to the data and metadata held by statistical offices. It focuses on ways in which statisticians can use Semantic Web technologies and standards in order to formalize, publish, document and link their data and metadata, and on how statistical methods can be applied on linked data. The statistical community shows growing interest in the Semantic Web. Initiatives have been launched to develop semantic vocabularies representing statistical classifications, discovery metadata, business models... Tools have been created by statistical organizations to support the publication of dimensional data conforming to the Data Cube W3C Recommendation. But statisticians still see challenges when using Semantic Web technologies: How can data and concepts be linked in a statistically rigorous fashion? How can we avoid fuzzy semantics leading to wrong analysis? How can we preserve data confidentiality? How can we use linked statistical data in machine learning models? The workshop will also cover the question of how to apply statistical methods to linked data, and how to develop new methods and tools for this purpose. Except for visualization techniques and tools, this question is still relatively unexplored.Property: http://purl.org/dc/elements/1.1/creatorhttps://w3id.org/scholarlydata/person/raphael-troncyhttps://w3id.org/scholarlydata/person/evangelos-kalampokishttps://w3id.org/scholarlydata/person/sarven-capadislihttps://w3id.org/scholarlydata/person/franck-cottonhttps://w3id.org/scholarlydata/person/armin-hallerProperty: http://purl.org/dc/elements/1.1/subjectlinked statistics ontology for statistics catalogue lists data cubeProperty: http://purl.org/dc/elements/1.1/title7th International Workshop on Semantic Statistics (SemStats 2019)Property: http://purl.org/ontology/bibo/authorListhttps://w3id.org/scholarlydata/authorlist/iswc-2019-workshop-30Property: http://xmlns.com/foaf/0.1/makerhttps://w3id.org/scholarlydata/person/armin-hallerhttps://w3id.org/scholarlydata/person/raphael-troncyhttps://w3id.org/scholarlydata/person/franck-cottonhttps://w3id.org/scholarlydata/person/evangelos-kalampokishttps://w3id.org/scholarlydata/person/sarven-capadisliProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#abstractThis workshop aims to explore and strengthen the relationship between the Semantic Web and statistical communities, to provide better access to the data and metadata held by statistical offices. It focuses on ways in which statisticians can use Semantic Web technologies and standards in order to formalize, publish, document and link their data and metadata, and on how statistical methods can be applied on linked data. The statistical community shows growing interest in the Semantic Web. Initiatives have been launched to develop semantic vocabularies representing statistical classifications, discovery metadata, business models... Tools have been created by statistical organizations to support the publication of dimensional data conforming to the Data Cube W3C Recommendation. But statisticians still see challenges when using Semantic Web technologies: How can data and concepts be linked in a statistically rigorous fashion? How can we avoid fuzzy semantics leading to wrong analysis? How can we preserve data confidentiality? How can we use linked statistical data in machine learning models? The workshop will also cover the question of how to apply statistical methods to linked data, and how to develop new methods and tools for this purpose. Except for visualization techniques and tools, this question is still relatively unexplored.Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasAuthorListhttps://w3id.org/scholarlydata/authorlist/iswc-2019-workshop-30Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#isPartOfhttps://w3id.org/scholarlydata/conference/iswc/2019/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#keywordcatalogue listsdata cubeontology for statisticslinked statisticsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#title7th International Workshop on Semantic Statistics (SemStats 2019)