%0 Conference Paper %1 heil2016towards %A Heil, Sebastian %A Wild, Stefan %A Krug, Michael %A Gaedke, Martin %B Joint Proceedings of the Posters and Demos Track of the 12th International Conference on Semantic Systems (SEMANTiCS 2016) %D 2016 %K myown %T Towards Reputation-Aware Expert Finding with Linked Open Data %X 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 reputation-aware 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.

@inproceedings{heil2016towards, 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 reputation-aware 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.}, added-at = {2016-09-16T14:37:56.000+0200}, author = {Heil, Sebastian and Wild, Stefan and Krug, Michael and Gaedke, Martin}, biburl = {https://www.bibsonomy.org/bibtex/2104d124313ed961c934949897b5ca36c/michikrug}, booktitle = {Joint Proceedings of the Posters and Demos Track of the 12th International Conference on Semantic Systems (SEMANTiCS 2016)}, interhash = {9b3c3c1c81a464b5849313ab3a92f0b9}, intrahash = {104d124313ed961c934949897b5ca36c}, keywords = {myown}, timestamp = {2016-09-16T14:39:42.000+0200}, title = {Towards Reputation-Aware Expert Finding with Linked Open Data}, year = 2016 }