Semantic query answering with time-series graphs

Leo Ferres; M. Dumontier; Natalia Villanueva-Rosales

doi:10.1109/EDOCW.2007.28

Semantic query answering with time-series graphs

Leo Ferres^*, M. Dumontier, Natalia Villanueva-Rosales

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

Statistical graphs are ubiquitous mechanisms for data visualization such that most, if not all, enterprises communicate information through their. 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.

Original language	English
Title of host publication	2007 Eleventh International IEEE EDOC Conference Workshop
Publisher	IEEE
ISBN (Print)	9780769533384
DOIs	https://doi.org/10.1109/EDOCW.2007.28
Publication status	Published - 2007
Externally published	Yes

Access to Document

10.1109/EDOCW.2007.28

Cite this

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title = "Semantic query answering with time-series graphs",

abstract = "Statistical graphs are ubiquitous mechanisms for data visualization such that most, if not all, enterprises communicate information through their. 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.",

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