In recent years, online knowledge curation on the internet has been lifted to a new level-online mass-collaboration. Knowledge bases such as Wikipedia and Wikidata are curated by huge communities and produce a vast amount of knowledge. Therefore, the prevailing challenge is to maintain a homogeneous structure in those knowledge bases to ensure efficient search capabilities, allow automated reasoning, and provide semantically linkable knowledge for the Linked Open Data cloud.
In this thesis we propose the SnoopyConcept which leverages recommender systems to support the user to homogenize knowledge already during the insertion process. The recommendations furthermore aim at increasing the quality and quantity of stored knowledge in collaborative information systems. In addition, we implement the universal SnoopyConcept in several domains and systems to evaluate and assess the recommender system based approach.