Navigation

Content Hotkeys
Distributed and Self-organizing Systems
Distributed and Self-organizing Systems
Teaser

PUBLICATION

CRAWL•E: Distributed Skill Endorsements in Expert Finding

Type

Conference Paper

Year

2014

Authors

heseba

wild

gaedke

Research Area

Web Engineering

Event

14th International Conference on Web Engineering

Published in

Proceedings of 14th International Conference on Web Engineering (ICWE2014)

ISBN/ISSN

9783319082448

Download

PDF

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.





Powered by DGS
Edit list (authentication required)

Press Articles