PUBLICATION
CRAWL•E: Distributed Skill Endorsements in Expert Finding
Type
Conference Paper
Year
2014
Authors
Dr.-Ing. Sebastian Heil
Research Area
Event
14th International Conference on Web Engineering
Published in
Proceedings of 14th International Conference on Web Engineering (ICWE2014)
ISBN/ISSN
9783319082448
Download
Abstract
Finding suitable workers for specific functions largely relies on human assessment. In web-scale environments this assessment exceeds human capability. Thus we introduced the CRAWL approach for Adaptive Case Management (ACM) in previous work. For finding experts in distributed social networks, CRAWL leverages various Web technologies. It supports knowledge workers in handling collaborative, emergent and unpredictable types of work. To recommend eligible workers, CRAWL utilizes Linked Open Data, enriched WebID-based user profiles and information gathered from ACM case descriptions. By matching case requirements against profiles, it retrieves a ranked list of contributors. Yet it only takes statements people made about themselves into account. We propose the CRAWL•E approach to exploit the knowledge of people about people available within social networks. We demonstrate the recommendation process by prototypical implementation using a WebID-based distributed social network.
Reference
Heil, Sebastian; Wild, Stefan; Gaedke, Martin: CRAWL•E: Distributed Skill Endorsements in Expert Finding. Proceedings of 14th International Conference on Web Engineering (ICWE2014), pp. 57-75, 2014.
