On Learnability of Constraints from RDF Data

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• 关键词：RDF constraints ; Linked data mining ; Data quality ; Data semantics
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9678
• 期：1
• 页码：834-844
• 全文大小：448 KB
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• 作者单位：Emir Muñoz (19) (20)
  
  19. Fujitsu Ireland Limited, Dublin, Ireland
  20. Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland
  
• 丛书名：The Semantic Web. Latest Advances and New Domains
• ISBN：978-3-319-34129-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9678

文摘

RDF is structured, dynamic, and schemaless data, which enables a big deal of flexibility for Linked Data to be available in an open environment such as the Web. However, for RDF data, flexibility turns out to be the source of many data quality and knowledge representation issues. Tasks such as assessing data quality in RDF require a different set of techniques and tools compared to other data models. Furthermore, since the use of existing schema, ontology and constraint languages is not mandatory, there is always room for misunderstanding the structure of the data. Neglecting this problem can represent a threat to the widespread use and adoption of RDF and Linked Data. Users should be able to learn the characteristics of RDF data in order to determine its fitness for a given use case, for example. For that purpose, in this doctoral research, we propose the use of constraints to inform users about characteristics that RDF data naturally exhibits, in cases where ontologies (or any other form of explicitly given constraints or schemata) are not present or not expressive enough. We aim to address the problems of defining and discovering classes of constraints to help users in data analysis and assessment of RDF and Linked Data quality.