Special Issue on Web Data Quality in IJSWIS

Call for papers
Special Issue on Web Data Quality
International Journal on Semantic Web and Information Systems


The standardization and adoption of Semantic Web technologies has resulted in an unprecedented volume of data being published as Linked Open Data (LOD). The integration across this Web of Data, however, is hampered by the ‘publish first, refine later’philosophy. This leads to various quality problems arising in the underlying data such as incompleteness, inconsistency and incomprehensibility. These problems affect every application domain, be it scientific (e.g., life science, environment), governmental or industrial applications.

This Special Issue is addressed to those members of the community interested in providing novel methodologies or frameworks in assessing, monitoring, maintaining and improving the quality of the Web of Data and also introduce tools and user interfaces which can effectively assist in the assessment. The benefits of such methodologies will not only help in detecting inherent data quality problems currently plaguing the Web of Data, but also provide the means to fix these problems and maintain the quality in the long run. Additionally, we also seek articles that help identify the current impediments in building real-world LOD applications


  • Web data and LOD quality concepts
  • Data quality dimensions and metrics for Web data and LOD quality
  • Web data and LOD quality methodologies
  • Data quality assessment frameworks
  • Evaluation of quality and trustworthiness in the web of data
  • (Semi-)automatic assessment in the web of data
  • Large-scale quality assessment of structured datasets
  • Validation of currently existing data quality assessment methodologies
  • Use-case driven quality assessment
  • Quality assessment leveraging background knowledge
  • Co-reference detection and dataset reconciliation
  • Data quality methodologies for linked open data
  • Evaluating quality of ontologies
  • Web data and LOD quality tools
  • Design and implementation of data quality monitoring, assessment and improvement tools
  • Quality exploration and analysis interfaces
  • Scalability and performance of tools
  • Monitoring tools
  • Case studies on Web data and LOD quality assessment and improvement
  • Web data and LOD quality benchmarks
  • Issues in LOD
  • Methods to acquire most relevant LOD datasets
  • Generating meaningful associations across LOD datasets
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