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.
- YAGO: YAGO is a large ontology constructed from WordNet, Wikipedia, and other sources. We develop YAGO together with the Database department of the Max Planck Institute for Informatics in Germany.
- AMIE: AMIE is a project to learn patterns and rules in ontologies. We conduct this project together with the Database department of the Max Planck Institute for Informatics in Germany.
- PARIS: PARIS is a project to learn mappings between ontologies. This initiative was born in the Webdam project.
- IBEX: IBEX is an approach to harvest entities such as people, commercial products, or books from the Web. We developed IBEX together with the Max Planck Institute for Informatics in Germany.
- DIVINA: DIVINA doesn’t have anything to do with ontologies. It just helps people find out whether their online accounts are secure.
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.
- scikit-network: scikit-network is a Python package for the analysis of large graphs (clustering, embedding, ranking).
- EUQLID: The EUQLID project investigates novel methods for managing and mining graph data.
The Social Web
The Web has evolved more and more into a social Web: content is produced and shared by users. In the DIG team, we follow and anticipate developments in this area.
- 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.
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 seeks to explain the relevance of situations or events to human minds. See http://www.simplicitytheory.science
- Relevance in natural language: The point is to retro-engineer methods to achieve meaningful and relevant speech from our understanding of human performance. Read this paper. Read more on this.
- Communication as social signalling: We apply game theory and social simulation to explore conditions in which providing valuable (i.e. relevant) information is a profitable strategy. Read this paper. Read more on this.
Machine Learning for IoT Data Streams
We investigate how to do machine learning in real time using Big Data, contributing to new open source tools:
- scikit-multiflow: a machine learning framework for multi-output/multi-label and stream data.
- 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.
Big Data & Market Insights
We focus in this project on Big data management and mining and their applications in digital marketing.
- Scalability of the algorithms on large sets of real data
- Context-aware recommender systems and predictive models: hotel booking, travel recommandation, Points of Interest …
- Social networks analysis and web information extraction: community detection, centrality, engagement rate …