Abstract
How to exploit knowledge to make better use of data is a timely issue at the crossroad of knowledge representation and reasoning, data management, and the semantic web. Knowledge representation is emblematic of the symbolic approach of Artificial Intelligence based on the development of explicit logic-based models processed by generic reasoning algorithms that are founded in logic. Recently, ontologies have evolved in computer science as computational artefacts to provide computer systems with a conceptual yet computational model of a particular domain of interest. Similarly to humans, computer systems can base decisions on reasoning about domain knowledge. And humans can express their data analysis needs using terms of a shared vocabulary in their domain of interest or of expertise. In this talk, I will show how reasoning on data can help to solve in a principled way several problems raised by modern data-centered applications in which data may be ubiquitous, multi-form, multi-source and musti-scale. I will also show how models and algorithms have evolved to face scalability issues and data quality challenges.
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