Machine Learning on Structured Data
The group focuses on combining Semantic Web and supervised Machine Learning technologies. The goal is to improve both quality and quantity of available knowledge by extracting, analysing, enriching and linking existing data. The group also provides several established open source tools, frameworks and demonstrators.
Distributed In-memory Analytics
The research group develops scalable analytics algorithms based on Apache Spark and Apache Flink for analysing large scale datasets by distributing the computational tasks in memory.
Deep learning models consist of multiple non-linear processing layers which allow for the hierarchical extraction of more and more abstract characteristics of the data and the discovery of the intricate structure in large data sets by building distributed representations.
Semantic Question Answering
The use of Semantic Web technologies led to an increasing number of structured data published on the Web. Despite the advances on question answering systems retrieving the desired information from structured sources is still a substantial challenge.
Geospatial analysis is the gathering, display, and manipulation of imagery, GPS, satellite photography and historical data, described explicitly in terms of geographic coordinates or implicitly, in terms of a street address, postal code, or forest stand identifier as they are applied to geographic models.
The group focuses on combining Semantic Web and Software Engineering technologies
- to develop novel methods, techniques, and tools that advance the way in which software is designed, synthetised and assessed;
- to ensure that our research results have a lasting impact in software development practice;
- to offer students an education that prepares them to take a leading role in complex software development projects; and
- to contribute to improve the competetiveness of the industry.