PUBLICATION
Towards Reputation-Aware Expert Finding with Linked Open Data
Type
Conference Paper
Year
2016
Authors
Dr.-Ing. Sebastian Heil
Research Area
Event
12th International Conference on Semantic Systems
Published in
Joint Proceedings of the Posters and Demos Track of the 12th International Conference on Semantic Systems - SEMANTiCS2016 and the 1st International Workshop on Semantic Change & Evolving Semantics (SuCCESS"16) co-located with the 12th International Conference on Semantic Systems (SEMANTiCS 2016)
ISBN/ISSN
ISSN 1613-0073
Download
Abstract
Distributed social networks allow creating new work patterns, addressing the workforce of a company as crowd. Here, finding suitable workers for specific functions is important for work quality, but largely relies on human assessment. In web-scale environments this assessment exceeds human capability. Linked open data has proven to be successful in providing semantic descriptions and discoverability of distributed resources. Hence, we leverage linked open data, so that each worker can have a semantic profile based on WebID and reference co-workers, skills, projects, etc. To recommend suitable experts for a given task, supporting systems are required, which use this profile data. In this paper, we extend our previous work on CRAWL towards reputationaware expert finding in distributed social networks. We outline three major aspects – Endorsements, Achievements and Meta Reputation – to achieve reputation awareness and report on our progress, showcase open challenges and present a roadmap for future work.
Reference
Heil, Sebastian; Wild, Stefan; Krug, Michael; Gaedke, Martin: Towards Reputation-Aware Expert Finding with Linked Open Data. Joint Proceedings of the Posters and Demos Track of the 12th International Conference on Semantic Systems - SEMANTiCS2016 and the 1st International Workshop on Semantic Change & Evolving Semantics (SuCCESS"16) co-located with the 12th International Conference on Semantic Systems (SEMANTiCS 2016), 2016.
