Identifying Knowledge Anchors in a Data Graph

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.

URI

http://hdl.handle.net/10454/10873

Version

Accepted manuscript

Citation

Al-Tawil M, Dimitrova V, Thakker D and Bennett B (2016) Identifying Knowledge Anchors in a Data Graph. In: Proceedings of the 27th ACM Conference on Hypertext and Social Media (HT '16) 10-13 Jul 2016, Halifax, Canada. ACM, New York, NY, USA: 189-194.

Link to published version

http://ceur-ws.org/Vol-1628/

Type

Conference paper