Formalization of Knowledge Centric Paradigms for Diversification of Web Search
Formalization of Knowledge Centric Paradigms for Diversification of Web Search

Formalization of Knowledge Centric Paradigms for Diversification of Web Search


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About the Book

With the increase in the density of information on the World Wide Web, it is the largest information repository that houses information with a high degree of heterogeneity. With the emergence of the digital era and information age where the internet is affordable and accessible by a large number of users', the Web is become the most widely used entity by millions of users' across the globe. The Web is drowning with data, flooded by information, while it is still scarce of useful information and knowledge. With the varying degree of diversity of data on the Web, it ranges from being structured, semi-structured, or even unstructured. The contents of the Web are intertwined together by means of web pages, multimedia contents, hyperlinks, and several useful repositories. However, the present-day Web is quite absurd in terms of its organization as the inflow of information is exponentially rising and is becoming immeasurable and immensely large to organize its structure. The main reason for the Web to be randomized and scattered is that there were no standards that were defined during the time of its inception. However, the standards were defined much later as the Web was growing at a scale larger than before. The Web is constantly changing and is the most dynamic entity in the real world which can be compared to a digital society which has no geography and boundaries.

Owing to the current structure of the intermediate Web which is transforming from a conventional Web 2.0 to a more organized and data intensified Web 3.0 or the Semantic Web, there is a mandated need for Web Search frameworks which would comply to the standards and the complex structure of the Semantic Web. The elicitation of the semantics from data that constitutes the Semantic Web on application of Semantic Artificial Intelligence can tackle the existing problems in Web Search and serve as a foundational basis for Semantic Search. The increasing availability of Linked Open Data and other standard knowledge sources like the Semantic Wikis in the present-day Web has prepared a way for knowledge centric Semantic Search. The Semantic Search is a web search paradigm which is a resultant of amalgamation of open domain knowledge and Semantic Artificial Intelligence to transform the conventional learning-based scheme to a computationally reliable inferential scheme to ensure technical feasibility in a highly linked and data intensified environment of the Semantic Web. Although several traditional web search schemes have solved the Personalization, Data Sparsity, Context Irrelevance, Synonymy, and the Polysemy problems, there are very few web search schemes which are semantically driven to comply with the demanding needs of the Web 3.0. Moreover, the Serendipity problem is one such problem which has been neglected and seldom attempted to be solved in conventional web search. The existing commercial search engines are evidentially biased even to the extent of supressing the actual essence of the query term and prioritizing leading brands in correlation with the query term, exhibiting a search query bias. There is a dire need for an unbiased search scheme to tackle the Serendipity problem and other associated problems in web search incorporating Semantic Intelligence to suit the linked structure of the Semantic Web. Furthermore, there is a demand for newer Semantic Artificial Intelligence techniques to be formulated for relevance computation and facilitating Semantic Search.


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Product Details
  • ISBN-13: 9798889952671
  • Publisher: Self Publisher
  • Publisher Imprint: Self Publisher
  • Height: 229 mm
  • No of Pages: 272
  • Spine Width: 14 mm
  • Width: 152 mm
  • ISBN-10: 8889952679
  • Publisher Date: 16 May 2023
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Weight: 417 gr


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Formalization of Knowledge Centric Paradigms for Diversification of Web Search
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