TY - GEN
T1 - Ontology-driven approach for knowledge discovery in academic databases
AU - Igoche, B.
AU - Matthew, O.
AU - Bednar, P.
AU - Gegov, A.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/3/24
Y1 - 2024/3/24
N2 - 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.
AB - 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.
KW - decision-making
KW - Hidden patterns
KW - Knowledge discovery
KW - Ontology-driven
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85189648837&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-55848-1_37
DO - 10.1007/978-3-031-55848-1_37
M3 - Conference contribution
AN - SCOPUS:85189648837
SN - 9783031558474
T3 - Lecture Notes in Networks and Systems
SP - 316
EP - 327
BT - Advances in Real-Time Intelligent Systems - Real-Time Intelligent Systems 2023
A2 - Pichappan, Pit
A2 - Rodriguez Jorge, Ricardo
A2 - Chung, Yao-Liang
PB - Springer Nature
T2 - 5th International Conference on Real Time Intelligent Systems, RTIS 2023
Y2 - 9 October 2023 through 11 October 2023
ER -