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Content archived on 2024-05-28

Knowledge Driven Data Exploitation

Objective

The development of innovative technology for the discovery new insights (such as trends) of digital data and real time guidance of using digital data is one of the priorities in the areas of semantic technologies. The first objective of this project is the identification of novel query generation techniques to facilitate experts to identify key dimensions within semantic data and to recommend users with queries related to their interests, with an emphasis on the development of innovative techniques for efficient and scalable query generation and query answering.

We first evaluate the query generation approaches with two use cases on electronic tender data and healthcare data. The most promising and effective approaches will be further investigated with the view of dealing with update of the data and relevant knowledge. For example, the EU Tenders Electronic Daily (TED, http://ted.europa.eu(opens in new window)) data is updated daily; bio-medical data regarding patients is constantly generated. The main goals of the projects are (1) the development of efficient and scalable query generation techniques with potential for dealing with streaming data and knowledge, (2) detailed studies of their use for exploiting both public data (such as TED data) and private data (such as that in the healthcare domain), with the future perspective of using these novel knowledge driven data analysis tools as diagnostic and guidance tools for domain experts and end users.

Fields of science (EuroSciVoc)

Topic(s)

Call for proposal

FP7-PEOPLE-2011-IAPP
See other projects for this call

Funding Scheme

MC-IAPP - Industry-Academia Partnerships and Pathways (IAPP)

Coordinator

THE UNIVERSITY COURT OF THE UNIVERSITY OF ABERDEEN
EU contribution
€ 913 835,00
Address
KING'S COLLEGE REGENT WALK
AB24 3FX Aberdeen
United Kingdom
Region
Scotland North Eastern Scotland Aberdeen City and Aberdeenshire
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data

Participants (4)