This track addresses two overlapping problem spaces: on the one hand, data dynamics and, on the other hand, quality and trust.
Data dynamics refers to a current and significant trend towards the continuous and fast production of data as well as the diffuse need to consume it in a timely fashion (a.k.a., being reactive) to act in time. This trend is highly visible in IoT, Industry 4.0, and Social Media, but it is emerging in many other industries. In this scenario, there is a clear need for new theoretical, analytical, and empirical solutions tailored to fast-evolving and heterogeneous data. The Semantic Web research landscape has already developed some of them in investigating Stream Reasoning, RDF streams, and extensions of SPARQL for data streams and complex events but there is still a long way to go before creating real-world impact.
Data quality becomes challenging with the increasing sizes and dynamicity of data. Many existing approaches mostly contain a customized data quality functionality to detect and analyze data quality issues within their own domain. However, this process is both data-intensive and computing-intensive and it is a challenge to develop fast and efficient algorithms that can handle large scale and dynamic knowledge graphs (KGs). New algorithms and metrics have to be designed, in particular, in order to deal with novel requirements related on one hand to the variety, volume and velocity issues of KGs and on the other hand to their structural characteristics. Moreover, none of the current approaches use the assessment to ultimately improve the quality in the long run.
In this call for paper, we invite novel contributions that address one of the two spaces or both at the same time.
As a new theme in 2021, ESWC also encourages the submission of negative results papers, which we also encourage in this track. Specific instructions for negative results papers can be found here.
Topics of Interest
Topics of interest include, but are not limited to
- Quality related
- Validation technologies: ShEx, SHACL, etc.
- Performance and scalability of QA approaches
- Knowledge graph quality for machine learning applications
- Extracting schemas from data (data profiling)
- Community-based knowledge graphs and quality: Wikidata, DBpedia, YAGO, etc.
- Quality of enterprise-based knowledge graphs
- Applications of validation languages: summarizing, transformation, subsetting, form generation, etc.
- Methods for knowledge graph and data cleansing and completion
- Knowledge graph curation and quality
- Real-time validation of knowledge graphs
- Trustworthiness assessment
- Stream related
- The role of semantic technologies (Linked Data, RDF, OWL, rules, etc.) in detecting, recognizing, describing, publishing, discovering, composing, tracking provenance and analysing data streams and events
- The role of semantic technologies in architectures, platforms, and middlewares for management and analysis of data streams and events
- Operational Semantics of Stream Processors, Complex Event processors, and Stream Reasoners
- Semantic streams & events at the edge and in the fog
- Federations of Stream processors, Complex Event Processors, Stream Reasoners and less dynamic data sources
- Experiences in IoT, Social Media, Data Center Monitoring, environmental monitoring, scientific research, smart cities, Context- and location-aware applications, etc
- Continuous ontological query answering and usage or reasoning in Complex Event Processing
- Temporal Reasoning and Time Ontology-Based Data Access for Dynamic Data
Information on deadlines and submission formats can be found here.
Emanuele Della Valle, Politecnico di Milano (POLIMI)
Anisa Rula, University of Milano-Bicocca (UNIMIB)