Exploiting Linked Open Data for Enhancing MediaWiki-based Semantic Organizational Knowledge Bases

Matthias Frank, Stefan Zander

Abstract

One of the main driving forces for the integration of Semantic Media Wiki systems in corporate contexts is their query construction capabilities on top of organization-specific vocabularies together with the possibility to directly embed query results in wiki pages. However, exploiting knowledge from external sources like other organizational knowledge bases or Linked Open Data as well as sharing knowledge in a meaningful way is difficult due to the lack of a common and shared schema definition. In this paper, we introduce Linked Data Wiki (LD-Wiki), an approach that combines the power of Linked Open Vocabularies and Data with established organizational semantic wiki systems for knowledge management. It supports suggestions for annotations from Linked Open Data sources for organizational knowledge bases in order to enrich them with background information from Linked Open Data. The inclusion of potentially uncertain, incomplete, inconsistent or redundant Linked Open Data within an organization’s knowledge base poses the challenge of interpreting such data correctly within the respective context. In our approach, we evaluate data provenance information in order to handle data from heterogeneous internal and external sources adequately and provide data consumers with the latest and best evaluated information according to a ranking system.

Download


Paper Citation


in Harvard Style

Frank M. and Zander S. (2017). Exploiting Linked Open Data for Enhancing MediaWiki-based Semantic Organizational Knowledge Bases.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, ISBN 978-989-758-272-1, pages 98-106. DOI: 10.5220/0006587900980106


in Bibtex Style

@conference{keod17,
author={Matthias Frank and Stefan Zander},
title={Exploiting Linked Open Data for Enhancing MediaWiki-based Semantic Organizational Knowledge Bases},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD,},
year={2017},
pages={98-106},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006587900980106},
isbn={978-989-758-272-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD,
TI - Exploiting Linked Open Data for Enhancing MediaWiki-based Semantic Organizational Knowledge Bases
SN - 978-989-758-272-1
AU - Frank M.
AU - Zander S.
PY - 2017
SP - 98
EP - 106
DO - 10.5220/0006587900980106