Community-driven Consolidated Linked DataAman Shakya (Tribhuvan University, Nepal), Hideaki Takeda (National Institute of Informatics, Japan) and Vilas Wuwongse (Asian Institute of Technology, Thailand)
Copyright © 2011. 31 pages.
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DOI: 10.4018/978-1-60960-593-3.ch009, ISBN13: 9781609605933, ISBN10: 1609605934, EISBN13: 9781609605940 Sample PDFCite Chapter
MLA
Shakya, Aman, Hideaki Takeda and Vilas Wuwongse. "Community-driven Consolidated Linked Data." Semantic Services, Interoperability and Web Applications: Emerging Concepts. IGI Global, 2011. 228-258. Web. 23 May. 2012. doi:10.4018/978-1-60960-593-3.ch009
APA
Shakya, A., Takeda, H., & Wuwongse, V. (2011). Community-driven Consolidated Linked Data. In A. Sheth (Ed.), Semantic Services, Interoperability and Web Applications: Emerging Concepts (pp. 228-258). Hershey, PA: Information Science Reference. doi:10.4018/978-1-60960-593-3.ch009
Chicago
Shakya, Aman, Hideaki Takeda and Vilas Wuwongse. "Community-driven Consolidated Linked Data." In Semantic Services, Interoperability and Web Applications: Emerging Concepts, ed. Amit Sheth, 228-258 (2011), accessed May 23, 2012. doi:10.4018/978-1-60960-593-3.ch009
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 | | TopAbstractUser-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 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. 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. TopComplete Chapter List|
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Adrian Mocan (SAP Research CEC, Germany1), Federico M. Facca (University of Innsbruck, Austria), Nikolaos Loutas (University of Macedonia, Greece), Vassilios Peristeras (National University of Ireland, Ireland), Sotirios K. Goudos (Aristotle University of Thessaloniki, Greece), Konstantinos Tarabanis (University of Macedonia, Greece)
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| A New Similarity Measure for Automatic Construction of the Unknown Word Lexical Dictionary
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| Extracting Concepts’ Relations and Users’ Preferences for Personalizing Query Disambiguation
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| The Berlin SPARQL Benchmark
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| Learning of OWL Class Expressions on Very Large Knowledge Bases and its Applications
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| Scalable Authoritative OWL Reasoning for the Web*
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Aidan Hogan (National University of Ireland – Galway, Ireland), Andreas Harth (Karlsruher Institut für Technologie, Germany), Axel Polleres (National University of Ireland – Galway, Ireland)
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| Enabling Scalable Semantic Reasoning for Mobile Services
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| Linked Data: The Story so Far
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9.
| Community-driven Consolidated Linked Data
(pages 228-258)
Aman Shakya (Tribhuvan University, Nepal), Hideaki Takeda (National Institute of Informatics, Japan), Vilas Wuwongse (Asian Institute of Technology, Thailand)
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| Searching Linked Objects with Falcons: Approach, Implementation and Evaluation
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| A URI is Worth a Thousand Tags: From Tagging to Linked Data with MOAT
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| An Idea Ontology for Innovation Management
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| Inductive Classification of Semantically Annotated Resources through Reduced Coulomb Energy Networks
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| A Comparison of Corpus-Based and Structural Methods on Approximation of Semantic Relatedness in Ontologies
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