Objectives & Topics

Dataset and data profiling and its use to facilitate discovery and retrieval in the Web Data represents an interdisciplinary challenge for enabling the Web-scale reuse and uptake of entity-centric structured data on the Web. PROFILES continues to be the only forum which brings together researchers from a variety of strongly related areas dealing with the assessment, profiling, characterisation and exploitation of entity-centric Web data. To this end, PROFILES’17 will gather novel works from the fields of semantic query interpretation and federated search for entity-centric Web data, dataset selection and discovery as well as automated profiling of datasets using scalable data assessment and profiling techniques. PROFILES’17 will equally consider both novel scientific methods and techniques for querying, assessment, profiling, discovery of distributed datasets as well as the application perspective, such as the innovative use of tools and methods for providing structured knowledge about datasets, their evolution and fundamentally, means to search and query Web Data. We will seek application-oriented, as well as more theoretical papers and position papers.

The topics of interest of PROFILES’17 include:

  • dataset profile representation (vocabularies, schemas)
  • novel applications and techniques for dataset profiling
  • automated approaches to dataset analysis and exploration
  • profiling and assessment of novel forms of entity-centric Web data (tables, markup, schema.org)
  • analysis/monitoring of dataset and graph dynamics
  • topic profiling of datasets
  • assessment of dataset schema conformance and evolution
  • dataset quality analysis for query routing
  • query routing taking into account relevance and quality of distributed datasets
  • semantic annotation and expansion for keyword queries over distributed Web data
  • keyword query interpretation and disambiguation for Web Data
  • fusing, cleaning, ranking and refining search results
  • scalability & performance of distributed data queries
  • novel applications using dataset profiles

 

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