Ontology-Driven Data Filtering Techniques in Scholarly Information Systems

Abstract

The ever-increasing volume of academic data in the digital world, available on various platforms, creates new challenges in extracting, organizing, and utilizing relevant educational materials. Conventional information retrieval systems based on keywords face challenges related to vagueness, polysemy, and relevance. In that context, ontology-based data filtering techniques have proven more effective and sophisticated within scholarly information systems. Driven by these challenges, ontology-based information systems enable machines to understand, interpret, and process information in a manner similar to humans, utilizing domain-specific knowledge. This paper outlines ontology-based approaches to data filtering and retrieval in scholarly environments. It examines how domain-specific ontologies improve the precision and recall in filtering scholarly articles, datasets, and citations by capturing meaningful relationships and contextual information. Using ontologies enables the automated classification or dynamic categorization of the content. The use of ontology also assists in term disambiguation and semantic enrichment of queries, which helps broaden the scope of searches. Various OWL (Web Ontology Language), RDF (Resource Description Framework), and SPARQL are detailed as frameworks and technologies that facilitate the implementation of these techniques. Ontology-driven Filtering in digital libraries, citation databases, and academic search engines is demonstrated in case studies. The examples illustrate increased user satisfaction, quicker retrieval of pertinent material, and precise intersectional exploration. Moreover, we address issues related to ontology design, scaling, and the ongoing need to revise academic arguments that evolve. The study concludes that ontology-based filtering approaches offer a new paradigm for modern scholarly information systems, enabling enhanced filtering capabilities to navigate intricate and voluminous datasets. Through the integration of semantic technologies into retrieval execution frameworks, these systems are better equipped to meet the needs of researchers, teachers, and educational organizations in a world characterized by an abundance of information.