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Web Intelligence: A Fuzzy Knowledge-Based Framework for the Enhancement of Querying and Accessing Web Data

Web Intelligence: A Fuzzy Knowledge-Based Framework for the Enhancement of Querying and Accessing Web Data

Jafreezal Jaafar, Kamaluddeen Usman Danyaro, M. S. Liew
Copyright: © 2016 |Pages: 23
ISBN13: 9781466698406|ISBN10: 1466698403|EISBN13: 9781466698413
DOI: 10.4018/978-1-4666-9840-6.ch033
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MLA

Jaafar, Jafreezal, et al. "Web Intelligence: A Fuzzy Knowledge-Based Framework for the Enhancement of Querying and Accessing Web Data." Big Data: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2016, pp. 711-733. https://doi.org/10.4018/978-1-4666-9840-6.ch033

APA

Jaafar, J., Danyaro, K. U., & Liew, M. S. (2016). Web Intelligence: A Fuzzy Knowledge-Based Framework for the Enhancement of Querying and Accessing Web Data. In I. Management Association (Ed.), Big Data: Concepts, Methodologies, Tools, and Applications (pp. 711-733). IGI Global. https://doi.org/10.4018/978-1-4666-9840-6.ch033

Chicago

Jaafar, Jafreezal, Kamaluddeen Usman Danyaro, and M. S. Liew. "Web Intelligence: A Fuzzy Knowledge-Based Framework for the Enhancement of Querying and Accessing Web Data." In Big Data: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 711-733. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9840-6.ch033

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Abstract

This chapter discusses about the veracity of data. The veracity issue is the challenge of imprecision in big data due to influx of data from diverse sources. To overcome this problem, this chapter proposes a fuzzy knowledge-based framework that will enhance the accessibility of Web data and solve the inconsistency in data model. D2RQ, protégé, and fuzzy Web Ontology Language applications were used for configuration and performance. The chapter also provides the completeness fuzzy knowledge-based algorithm, which was used to determine the robustness and adaptability of the knowledge base. The result shows that the D2RQ is more scalable with respect to performance comparison. Finally, the conclusion and future lines of the research were provided.

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