CRAWL.E: Distributed skill endorsements in expert finding

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 variousWeb 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 for by prototypical implementation using a WebID-based distributed social network.