Ontologies and Knowledge Management

An ontology is a computer-processable collection of knowledge about the world. We are concerned with RDF Ontologies — i.e. with ontologies in the form of graphs. We construct and mine such ontologies.

Large Scale Data Mining

Graphs are a near-universal way to represent data. We are concerned with mining graphs for patterns and properties. Our particular focus is on the scalability of such approaches.

  • Graph Mining: We are concerned generally with mining properties of graphs.
  • EUQLID: The EUQLID project investigates novel methods for managing and mining graph data.
  • Chair Big Data: The Chair “Big Data and E-commerce Applications” is concerned with recommender systems, large graphs management and mining.
  • NormAtis: The NormAtis project is concerned with Web information extraction, data integration, uncertain data management.

The Social Web

The Web has evolved more and more into a social Web: content is produced and shared by users. In the DBWeb team, we follow and anticipate developments in this area.

  • Logo of ARCOMEMARCOMEM: Arcomem is about memory institutions like archives, museums, and libraries in the age of the Social Web. The project aims to transform archives into collective memories that are more tightly integrated with their community of users and to exploit Social Web and the wisdom of crowds to make Web archiving a more selective and meaning-based process
  • Community detection: We are investigating means to detect and distinguish social communities on the Web.
  • Social Relations: We investigate the optimal investment in social relations from a theoretical point of view.
  • NormAtis: The NormAtis project is concerned with Web information extraction, data integration, uncertain data management.

Language and relevance

Computer science is not just about computers. In this area of research, we investigate how humans reason, and what this implies for machines.

  • Simplicity Theory: Simplicity theory is a cognitive theory that seeks to explain the attractiveness of situations or events to human minds.
  • Natural Language and cognition: We investigate semantics and relevance from a cognitive science point of view.

IoT Big Data Stream Mining

We investigate how to do machine learning in real time using Big Data, contributing to new open source tools:

  • MOA: Massive Online Analytics, the most popular framework for mining data streams, implemented in Java.
  • Apache SAMOA: Scalable Advanced Massive Online Analytics, an open source framework for data stream mining on the Hadoop Ecosystem.