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Please use this identifier to cite or link to this item: http://hdl.handle.net/2262/41257

Title: Leveraging Domain Expertise to Support Complex, Personalized and Semantically Meaningful Queries Across Separate Data Sources
Other Titles: The IEEE Fourth International Conference on Semantic Computing
Author: HAMPSON, CORMAC
CONLAN, OWEN
HAMPSON, CORMAC
Sponsor: Irish Research Council for Science Engineering and Technology
Author's Homepage: http://people.tcd.ie/hampsonc
http://people.tcd.ie/hampsoc
http://people.tcd.ie/owconlan
http://people.tcd.ie/hampsonc
Keywords: Semantic Attributes
Domain Experts
Complex Queries
Data Exploration
Issue Date: 2010
Publisher: IEEE
Citation: Cormac Hampson, Owen Conlan, Leveraging Domain Expertise to Support Complex, Personalized and Semantically Meaningful Queries Across Separate Data Sources, The IEEE Fourth International Conference on Semantic Computing, Pittsburgh, PA, USA, September 22 - 24, IEEE, 2010, 305 - 308
Abstract: Abstract—Almost all information domains have witnessed an exponential increase in the amount of structured data available. However, there is still a lack of support for ordinary users to create complex queries spanning multiple information sources. Until this occurs the real benefits of having such a proliferation of metadata will not be realized by the general public. This paper describes SARA (Semantic Attribute Reconciliation Architecture), which is a framework that helps users leverage expert knowledge to discover relevant information, and to draw correlations across separate information sources. These sources can be in various data formats, and are accessed by users in a consolidated fashion. Users are supported in their information exploration with the knowledge of experts, which they can further tailor to better suit their needs. SARA offers tools and support for domain experts with no computing experience to encode their expertise, thus opening up SARA’s use to almost any domain where rich metadata is available. This paper discusses the SARA framework in detail, as well as describing the applications to which it has been successfully applied in a number of different domains.
Description: PUBLISHED
Pittsburgh, PA, USA
URI: http://hdl.handle.net/2262/41257
Appears in Collections:Computer Science (Scholarly Publications)

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