Community-driven Consolidated Linked Data Community-driven Consolidated Linked Data Technologies Used

User-generated content can help the growth of linked data. However, there are a lack of interfaces enabling ordinary people to author linked data. Secondly, people have multiple perspectives on the same concept and different contexts. Thirdly, there are not enough ontologies to model various data. Therefore, the authors of this chapter propose an approach to enable people to share various data through an easyto-use social platform. Users define their own concepts and multiple conceptualizations are allowed. These are consolidated using semi-automatic schema alignment techniques supported by the community. Further, concepts are grouped semi-automatically by similarity. As a result of consolidation and grouping, informal lightweight ontologies emerge gradually. The authors have implemented a social software system, called StYLiD, to realize the approach. It can serve as a platform motivating people to bookmark and share different things. It may also drive vertical portals for specific communities with integrated data from multiple sources. Some experimental observations support the validity of the approach. DOI: 10.4018/978-1-60960-593-3.ch009