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, classification, ranking).
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 Data Streams
We investigate how to do machine learning in real time, 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 on 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 …