Elena Simperl, Karlsruhe Institute of Technology, Germany
Track Program Committee
Harith Alani, Knowledge Media Institute, OU, UK
Sören Auer, Universität Leipzig, Germany
Chris Bizer, University of Mannheim, Germany
Oscar Corcho, UPM, Spain
Mariano Consens, University of Toronto, Canada
Gianluca Correndo, University of Southampton, UK
Richard Cyganiak, NUIG, Ireland
Ying Ding, Indiana University, USA
John Domingue, Knowledge Media Institute, OU, UK
Fabien Gandon, INRIA, France
Birte Glimm, University of Ulm
Tudor Groza, University of Queensland, Australia
Michael Hausenblas, NUIG, Ireland
Tom Heath, Talis, UK
Pascal Hitzler, Kno.e.sis, Wright State University, USA
Aidan Hogan, NUIG, Ireland
Spyrous Kotoulas, IBM, Ireland
Craig Knoblock, USC, USA
Jens Lehmann, Universität Leipzig, Germany
Vanessa Lopez, IBM, Ireland
Pablo Mendes, Open Knowledge Foundation, Germany
Natasha Noy, SMI, Stanford University, USA
Alexandre Passant, seevl.net, Ireland
Heiko Paulheim, Technische Universität Darmstadt, Germany
Matthew Rowe, University of Lancaster, UK
Manuel Salvadores, SMI, Standord University, USA
Aanod Sane, Yahoo, USA
Amit Sheth, Kno.e.sis, Wright State University, USA
Milan Stankovic, Hypios, France
Hideaki Takeda, National Institute of Informatics, Japan
Jamie Taylor, Google, USA
Nicolas Torzec, Yahoo! Labs, USA
Jacco van Ossenbruggen, Centrum voor Wiskunde en Informatica,
The Netherlands
Denny Vrandecic, Wikimedia, Germany
Goals
Linking data - and not just conventional Web documents - is one of
the core building blocks of the Semantic Web. As semantic standards and
technologies become mainstream - most prominently through developments
such as Google's Knowledge Graph, Facebook's Open Graph Protocol, the
schema.org initiative, and the Linked Open Data Cloud community
movement - we experience a rapid growth in the amount, quality, and
variety of usage scenarios for linked data sets. More and more
organizations in the public and private domains are not just publishing
their data according to Linked Data principles, but use the resulting
Web-wide wealth of resources, rich in meaning and structure, to expand
the functionality of their information-intensive applications.
The track focuses on research addressing challenges and opportunities
associated with realizing and using Web-scale, distributed semantic
data graphs, covering all aspects of the linked data management life
cycle, including the creation of new linked Data, interlinking, entity
extraction, enrichment of traditional Web content with semantic
annotations, combinations of linked and other types of data, quality
analysis, evolution and dynamics, and consumption through query
processing and mining.
Topics of interest
Topics of interest include, but are not limited to
Publication of linked data
Data set dynamics and evolution
Data interlinking
Data set curation
Semantic annotation and markup
Algorithms for linking textual and data sources and entities
Semantic social network representation and analysis
Automatic and socially inspired algorithms for linked data
management
Scalability of graph-based algorithms and infrastructures
Data mining for linked data
User interfaces and user/social interactions for linked data
Provenance, privacy, and rights management for linked data
