Al-Tawil, M, Dimitrova, VG, Thakker, D et al. (1 more author) (2016) Identifying Knowledge Anchors in a Data Graph. In: Proceedings of the 27th ACM Conference on Hypertext and Social Media. 27th ACM Conference on Hypertext and Social Media, 10-13 Jul 2016, Halifax, Canada. ACM , pp. 189-194. ISBN 978-1-4503-4247-6
Abstract
The recent growth of the Web of Data has brought to the fore the need to develop intelligent means to support user exploration through big data graphs. To be effective, approaches for data graph exploration should take into account the utility from a user's point of view. We have been investigating knowledge utility -- how useful the trajectories in a data graph are for expanding users' knowledge. Following the theory for meaningful learning, according to which new knowledge is developed starting from familiar entities (anchors) and expanding to new and unfamiliar entities, we propose here an approach to identify knowledge anchors in a data graph. Our approach is underpinned by the Cognitive Science notion of basic level objects in domain taxonomies. Several metrics for extracting knowledge anchors in a data graph, and the corresponding algorithms, are presented. The metrics performance is examined, and a hybridization approach that combines the strengths of each metric is proposed.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 ACM. This is an author produced version of a paper published in Proceedings of the 27th ACM Conference on Hypertext and Social Media. |
Keywords: | Graph-based database models; basic level entities; information exploration |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Funding Information: | Funder Grant number EU - European Union 257184 |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Jul 2016 11:53 |
Last Modified: | 13 Apr 2017 09:11 |
Published Version: | http://dx.doi.org/10.1145/2914586.2914637 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/2914586.2914637 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:102856 |