At the Unit for Reasoning and Querying at INSIGHT Galway, we focus on scalable and interoperable ways of transforming web data streams into actionable knowledge, leveraging Linked Open Data and Standards for data representation and management. At the processing level, we tackle crucial challenges for enabling IoT-intelligence, including large-scale stream query processing and optimization, hybrid reasoning involving inductive learning and deduction processes, quality-aware query federation, adaptive stream processing, stream reasoning and the application of those in Smart Cities, eHealth, Smart Farming and Enterprise Communication Systems.
Our goal is to find the right trade-off between expressive knowledge representation and efficient, scalable reasoning and querying techniques in the open, distributed environment of the Semantic Web. The focus is on improving the scalability and investigating the adequacy of traditional reasoning and query answering techniques for the Web, where several classical assumptions no longer hold as data is heterogeneous, distributed, possibly incomplete or contradictory, structured in different levels of granularity, varying in levels of trust and accessible only by following implicit or explicit security policies. The objectives include the definition of expressive query and rules languages; the investigation of the interplay between ontology languages, query languages and rules languages; efficient search and inferencing; scalable, distributed reasoning; closing the gaps between the RDF and XML worlds; supporting security and trust assessment on the Web by investigating adequate representation and reasoning techniques for assessing and negotiating trust; and dealing with dynamic, conflicting, uncertain and temporally changing metadata.