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.