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.
TopWeb Intelligence
Web information has great impact in human life especially in the domain of world knowledge such as uncertainty and imprecision. The Web intelligence constitutes the usage of WWW as a phenomenon of retrieving information from the storage efficiently. It acts as an agent in which the machine can reason and conveys the message using Web tools. The agent goes round and integrates the resource which finally presents the information to user through a Web page. The resources or things depend on RDF that links the entire concept together through Uniform Resource Identifier (URI). Web intelligence is a well-known research area which converges subjects such as artificial intelligence, databases, Web science, Semantic Web, and information retrieval (Berners-Lee et al., 2006; Camacho et al., 2013; Shadbolt & Berners-Lee, 2008; Shroff, 2013; Williams et al., 2014). Therefore, reasoning in Web data is the first step in finding the solution of a problem in the knowledge-based system.
Key Terms in this Chapter
Web Data: In this chapter, the Web data is the data that comes from large or diverse number of sources. Web data are developed with the help of Semantic Web tools such as RDF, OWL, and SPARQL. Also, the web data allows sharing of information through HTTP protocol or SPARQL endpoint.
Big Data Management: This is the process of managing, administering or organizing large volumes of both structured and unstructured data.
Satisfiablility: In this chapter, satisfiablity is the ability of the Web data concepts that are be able to test the value of the semantic query of atomic concepts. Similarly, satisfiability determines the variables of the given boolean.
Veracity: Veracity can be described as the quality of trustworthiness of the data. In other wards, veracity is the consistency in data due to its statistical reliability. It is also among the five dimentions of big data which are volume, velocity, value, variety and veracity.
Completeness: In Artificial Intelligence (AI), completeness theorem is among the methods used for checking the validity of axioms and logical inference in the knowlegde base. However, a knowledge base is said to be complete if no formular can be added in the knowledge base.
Gödel’s Semantics: In mathematical logic, Gödel’s completeness theorem is a fundamental theorem that relies on the semantic completeness of first order logic. In the proof of algorithm, when I is satisfiable, it shows the logic is decidable under Gödel’s semantics.
Fuzzy Knowledge Base: In fuzzy logic systems, the fuzzy knowledge base represents the facts of the rules and linguistic variables based on the fuzzy set theory so that the knowledge base sytems will allow approximate reasoning.
Web Intelligence: The Web intelligence constitutes the usage of world wide web (WWW) as a phenomenon of retrieving information from data storage in an intelligent efficient manner. Web intelligence is a well-known research area which converges subjects such as artificial intelligence, databases, Web science, Semantic Web, and information retrieval.