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Community-Driven Linked Data Authoring and Production of Consolidated Linked Data

Community-Driven Linked Data Authoring and Production of Consolidated Linked Data

Aman Shakya, Hideaki Takeda, Vilas Wuwongse
Copyright: © 2009 |Volume: 5 |Issue: 3 |Pages: 26
ISSN: 1552-6283|EISSN: 1552-6291|ISSN: 1552-6283|EISBN13: 9781616920579|EISSN: 1552-6291|DOI: 10.4018/jswis.2009081902
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MLA

Shakya, Aman, et al. "Community-Driven Linked Data Authoring and Production of Consolidated Linked Data." IJSWIS vol.5, no.3 2009: pp.23-48. http://doi.org/10.4018/jswis.2009081902

APA

Shakya, A., Takeda, H., & Wuwongse, V. (2009). Community-Driven Linked Data Authoring and Production of Consolidated Linked Data. International Journal on Semantic Web and Information Systems (IJSWIS), 5(3), 23-48. http://doi.org/10.4018/jswis.2009081902

Chicago

Shakya, Aman, Hideaki Takeda, and Vilas Wuwongse. "Community-Driven Linked Data Authoring and Production of Consolidated Linked Data," International Journal on Semantic Web and Information Systems (IJSWIS) 5, no.3: 23-48. http://doi.org/10.4018/jswis.2009081902

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Abstract

User-generated content can help the growth of linked data. However, we lack interfaces enabling ordinary people to author linked data. Secondly, people have multiple perspectives on the same concept and different contexts. Thirdly, not enough ontologies exist to model various data. Therefore, we propose an approach to enable people to share various data through an easy-to-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. We have implemented social software, called StYLiD, to realize our 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. Experimental observations support the validity of our approach.

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