Ontology-driven approach for knowledge discovery in academic databases

B. Igoche*, O. Matthew, P. Bednar, A. Gegov

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Knowledge discovery in academic databases is vital in facilitating informed decision-making and deeper insights within the academic domain. However, traditional approaches often need to be revised regarding the accuracy, interpretability, and usability of the discovered knowledge. This paper proposes an ontology-driven approach to knowledge discovery in academic databases. By leveraging ontologies, which provide a formal representation of domain-specific knowledge, this approach enhances the precision and context awareness of the discovery process. Integrating ontologies with academic databases establishes a semantic link, enabling more accurate and interpretable queries. Additionally, ontology enriches the knowledge discovery process, facilitating the identification of hidden patterns, relationships, and insights in the data. The ontology’s concepts and relationships represent the discovered knowledge, allowing for better interpretation and understanding. In addition, visualization techniques can be employed to enhance the usability and interpretability of the knowledge. Through case studies and examples, we demonstrate the effectiveness of the ontology-driven approach in uncovering meaningful knowledge in academic databases. This approach has the potential to support evidence-based decision-making, foster collaborations, and contribute to advancements in the academic field.

Original languageEnglish
Title of host publicationAdvances in Real-Time Intelligent Systems - Real-Time Intelligent Systems 2023
EditorsPit Pichappan, Ricardo Rodriguez Jorge, Yao-Liang Chung
PublisherSpringer Nature
Pages316-327
Number of pages12
ISBN (Electronic)9783031558481
ISBN (Print)9783031558474
DOIs
Publication statusPublished - 24 Mar 2024
Event5th International Conference on Real Time Intelligent Systems, RTIS 2023 - Luton, United Kingdom
Duration: 9 Oct 202311 Oct 2023

Publication series

NameLecture Notes in Networks and Systems
Volume950
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference5th International Conference on Real Time Intelligent Systems, RTIS 2023
Country/TerritoryUnited Kingdom
CityLuton
Period9/10/2311/10/23

Keywords

  • decision-making
  • Hidden patterns
  • Knowledge discovery
  • Ontology-driven
  • Visualization

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