Description
Linked Data (LD) refers to a set of best practices for exposing, sharing, and connecting data on the Web, while also referring to the ecosystem of data being released in this form. This paradigm has been successfully used in an increasing number of applications in a wide range of domains (media, live science, e-Government, digital humanities, linguistics, etc.). However, a number of research challenges still need to be addressed to further increase take-up and adoption of LD techniques. This track invites research submissions addressing the generation/extraction of LD from other types of data sources, the generation, maintenance and curation of links within and across datasets, scalable query and storage mechanisms, quality assessment and management, curation and validation, effective publishing methodologies, efficient consumption, access-restricted querying, as well as inferencing over LD. This track calls for research submissions advancing the state-of-the-art in the LD field, in particular related to the following, non-exhaustive list of topics of interest:
Topic of Interest
Topics of interest include but are not limited to:
- Consumption and publication of Linked Data (LD)
- Extraction, linking, and integration of LD
- Creation, storage, and management of LD
- Searching, querying, and reasoning over decentralized LD
- Dataset profiling and description
- Data quality, validation, and data trustworthiness
- Dynamics and evolution of LD
- Analyzing, mining, and visualizing LD
- Scalability issues relating to Linked Data
- Provenance, privacy, and rights management
- Leveraging RDFa, JSON-LD,Microdata, and other embedded formats
- Database, IR, NLP and AI technologies for LD
Track Chairs
Ricardo Usbeck, Paderborn University, Paderborn, Germany
Aidan Hogan, Universidad de Chile, Santiago, Chile
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