Optimizing Keyword Querying on Structured Knowledge Bases

Year : 2024 | Volume :11 | Issue : 02 | Page : 28-50
By

Ashokkumar Gurusamy,

Vinod Kumar,

Piyush Sharma,

  1. Principal Software Engineer Fidelity Investments United States
  2. Vice President GAVS Technologies New Jersey United States
  3. Associate Developer Groupsoft Us Inc United States

Abstract

As the digital landscape burgeons with unstructured information, the need for structured knowledge bases becomes paramount. The Semantic Web employs Resource Description Framework and SPARQL as a cornerstone in organizing information. This work explores keyword querying approaches on knowledge bases such as Freebase, emphasizing their usability challenges, particularly in complex queries. Existing approaches, while valuable, exhibit limitations in handling intricate queries with multiple joins. The proposed Entity Relationship Keyword Querying framework addresses this shortfall, enhancing usability and capability. This research outlines the Resource Description Framework/SPARQL foundations, surveys related works, presents Entity Relationship Keyword Querying, and discusses its potential enhancements. The work concludes with a prototype implementation summary, paving the way for future refinements in keyword querying on extensive knowledge bases.

Keywords: Semantic web, entity relationship keyword querying, ERKQ, resource description framework, RDF, SPARQL, prototype implementation, keyword querying

[This article belongs to Journal of Web Engineering & Technology(jowet)]

How to cite this article: Ashokkumar Gurusamy, Vinod Kumar, Piyush Sharma. Optimizing Keyword Querying on Structured Knowledge Bases. Journal of Web Engineering & Technology. 2024; 11(02):28-50.
How to cite this URL: Ashokkumar Gurusamy, Vinod Kumar, Piyush Sharma. Optimizing Keyword Querying on Structured Knowledge Bases. Journal of Web Engineering & Technology. 2024; 11(02):28-50. Available from: https://journals.stmjournals.com/jowet/article=2024/view=160608

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Regular Issue Subscription Original Research
Volume 11
Issue 02
Received June 26, 2024
Accepted July 23, 2024
Published August 2, 2024
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