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Data, Intelligence and Graphs (DIG) is a group of researchers at LTCI, Télécom ParisTech who study the fundamental issues raised in data and knowledge management systems, graph mining and artificial intelligence. Research interests cover theoretical foundations of data intelligence and graph systems, practical solutions and applications, as well as cognitive aspects. The group was formerly known as DBWeb Team.

DIG has strong academic and industrial collaborations:


Research

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.

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:

  • 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 …

People

Senior

Post-docs

PhD candidates

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Former members

News

The fundamentals of language: why do we talk? – Jean-Louis Dessalles, Télécom ParisTech

Article appeared at IMT Live Labs on 27 September 2017  https://www.imt.fr/en/entrailles-langage-parlons/ Human language is a mystery. In a society where information is so valuable, why do we talk to others without expecting anything in return? Even more intriguing than this are the processes determining communication, whether that be a profound debate or a spontaneous conversation with an …

Contact

Télécom ParisTech
46 Rue Barrault, 75013 Paris
albert.bifet@telecom-paristech.fr


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