BOREAL: Représentation de Connaissances et Langages à Base de Règles pour Raisonner sur les Données

BOREAL : Knowledge Representation and Rule-based Languages for Reasoning on Data

Current information systems are grounded on the exploitation of data coming from an increasing number of heterogeneous sources. Coping with the variety of data requires paradigms for effectively accessing and querying information that adapt to the different types of sources, as well as declarative high-level languages to drive the data processing and data quality tasks. The BOREAL team focuses on the study of foundational and applied issues of reasoning, in a context of data variety. The team builds upon its expertise in knowledge representation and automated reasoning to devise novel techniques for heterogeneous and federated data management which leverage in particular on expressive rule languages.

The team focuses on a set of issues related to knowledge-based data management which include:

• Foundations of rule languages (Existential Rules, Description Logics).
• Algorithms and optimizations for reasoning on data.
• Architectures and rule languages for heterogeneous data integration.
• Inconsistency handling in query answering.
• Quality of knowledge-based data integration systems.
• Explanation of reasoning