Objectives & Topics

The PROFILES’16 workshop aims to become a highly interactive research forum for researchers. PROFILES’16 will bring together researchers and practitioners in the fields of Semantic Web and Linked Data, Databases, Semantic Search, Text Mining, NLP as well as Information Retrieval. PROFILES’16 will gather novel works from the fields of semantic query interpretation and federated search for Linked Data, dataset selection and discovery as well as automated profiling of datasets using scalable data assessment and profiling techniques. PROFILES’16 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 distributed datasets, their evolution and fundamentally, means to search and query the Web of Data. We will seek application-oriented, as well as more theoretical papers and position papers.

The main areas of interest of PROFILES’16 include, but are not limited to:

Dataset/endpoint analysis, profiling and discovery:

  • dataset profile representation (vocabularies, schemas)
  • novel applications and techniques for dataset profiling
  • automated approaches to dataset analysis and exploration
  • analysis/monitoring of dataset and graph dynamics
  • topic profiling of datasets
  • assessment of dataset schema conformance and evolution
  • dataset quality analysis for query routing

Distributed semantic search:

  • query routing taking into account relevance and quality of distributed datasets
  • semantic annotation and expansion for keyword queries
  • keyword query interpretation and disambiguation for Linked Data
  • fusing, cleaning, ranking and refining search results
  • scalability & performance of distributed data queries
  • novel applications for federated search over Linked Data
  • question answering over distributed linked datasets
  • interlingual question answering over Linked Data