Natalia Villanueva Rosales
Statistical graphs are ubiquitous visual representations of data. Most (if not all) enterprises communicate information through them. However, many graphs are stored as unstructured images or proprietary binary objects, making them difficult to work with beyond the reports in which they are embedded. While graphs can be mapped to more common XML representations, these lack expressive semantics to discover new knowledge about them or to answer queries at various levels of granularity. Semantic Web provides a framework to annotate data that a machine can understand and reason about. During this presentation, the results of applying Semantic Web technologies to statistical graphs will be described. This approach facilitates the representation, exchange, reasoning and query answering of statistical graph data.