NEW: KEYNOTE CONFIRMED: Professor Panos Kalnis on “How to build scalable SPARQL engines for Big RDF data”
JOURNAL SPECIAL ISSUE: SEDSEAL papers to appear in a special issue of the Data int. journal
25 – 27 May, 2018, Rhodes, Greece
Recent advances in availability of information on the Internet, storage space and web generated content have paved the way for the advent of Big Data. The well-known 4 Vs (Velocity, Variety, Volume, Value) that characterize Big Data can find a match in intelligent ways for management, manipulation and value-extraction. It is widely acknowledged that the recent surge in AI and especially machine learning is exactly due to these advancements. The Semantic Web can offer a well-studied, although ever advancing, toolbox that can address Big Data requirements and contribute towards their meaningful analysis. Still, there are often issues that need to be tackled with like bootstrapping, efficiency and standardized business processes for semantic analytics to achieve satisfactory results. To this end, machine- and deep-learning techniques, while being shunned in the past, have been shown to have considerable contributions towards Big Data analytics and to overcome Semantic Web inherent limitations.
Therefore, the aim of Semantics in the Deep: Semantic Analytics for Big Data workshop is to bring together researchers and practitioners to look deeper into how Semantic Web technologies can contribute towards Big Data analytics. This can be achieved either by extracting value out of these data (through reasoning), creating sustainable ontology models, offering a solid foundation for deploying learning techniques or anything in between.