The Database community has increasingly become interested in the Semantic Web, and the two communities have enjoyed a joint growth in the past few years. We are witnessing an increase in the amount of structured data published online, such as HTML or XML tables, linked data as RDF or JSON-LD collections, embedded microformats, social graphs, and smart sensors. Semantifying, publishing, curating and managing such types of data in terms of entities and properties has been incorporated in a variety of research and commercial projects and settings, that involve, among others, web content harvesting, knowledge representation, knowledge extraction and enrichment, record and entity linkage, and searching, reasoning and analyzing structured web data and social data.
Furthermore, RDF triple stores and SPARQL query engines have attracted increasing interest from database researchers that has led to novel algorithms, methods and techniques for issues such as query optimization and efficient storage of huge data sizes and workloads. This field has also seen advances in distributed and cloud query processing on large scale datasets using MapReduce, and at the same time relational support of RDF and SPARQL has become more efficient. This allows for the application of existing and well-established relational techniques on semantic web contexts. Moreover, commercial interest in semantic web data has been shown by major companies and projects such as Google’s Knowledge Graph, Facebook’s Open Graph, BestBuy, BBC and Thomson Reuters.
While these indications show that the vision of the semantic web has become an established reality through a series of decade-long methodological advances, the community is shifting its attention to applying scaling methods for handling and managing very large collections of data and workloads. Issues such as query optimization, storage, query processing, record linkage, and semantic disambiguation and grounding form excellent candidates for scalable algorithms and methods of data management in the context of Big Data. Moreover, the proliferation of online social networks and media has brought to light new challenges for semantic technologies due to their large-scale, noisy, and real-time nature. These challenges can benefit from the expertise and insight of the database community that traditionally has experience handling similar issues.
DESWeb is focused on providing a forum that brings together these two separate communities, by highlighting their common interests and promoting mutual advancement and cross-fertilization. Given the recent explosion of structured data available on the Web and associated challenges with respect to what is commonly referred to as the 3V’s (Volume, Variety and Velocity) the workshop’s aim is more timely than ever.
The DESWEB workshop calls for short papers (of up to 6 pages, IEEE format) on research results and ongoing projects in the conjunction of these thematic areas. Topics of interest include but are not limited to:
- Ontologies and data integration
- Semantic search
- Semantic grounding of raw data
- Linked Data, including Privacy in Linked Data and its impact on data management
- Reasoning over Web data and services
- Semantic authoring and crowdsourcing
- Semantic recommender systems
- Generation and aggregation of social semantics
- Ontologies and data models for social data representation and analysis
- Social semantic recommendations
- Distributed query processing and optimization
- Data quality, profiling, and uncertainty
- Data mining and knowledge discovery
- Data provenance and trust
- Data versioning, evolution, change detection and representation
- Innovative use of semantic technologies (e.g., on scientific, healthcare, environmental, egovernment data etc.)
and all other topics at the crossroads of Semantic Web and Data Engineering research.
We also encourage the submission of papers on visions and challenges, focusing on radical and novel ideas rather than established approaches, as well as difficult and open problems rather than solutions.