Semantic Query Answering with Time-Series Graphs

Statistical graphs are ubiquitous mechanisms for data visualization such that 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. This paper describes an OWL ontology that facilitates the representation, exchange, reasoning and query answering of statistical graph data. We illustrate the advantages of using an ontological approach to discover and query about time-series statistical graphs.