Designing ontology-based search systems for research articles

The process of conducting scientific literature reviews is becoming increasingly complex and time-consuming due to the rapid expansion of available research. Popular academic search engines offer limited filtering capabilities and suffer from low precision. Machine learning-enhanced approaches tend to target rather specific areas, and novel approaches based on generative artificial intelligence suffer from hallucinations. Drawing on information foraging theory, this article presents a design science research project aimed at generating design knowledge for developing domain-specific search systems for research articles. Our contributions include: (1) integrating domain ontologies with large language models to design ontology-based search systems, (2) generating descriptive design knowledge by exploring the problem space, (3) generating prescriptive design knowledge for developing domain-specific search systems, and (4) presenting an ontology-based search engine prototype. Our results indicate that the proposed solution supports researchers in conducting literature reviews by increasing information gain while reducing interaction costs. • Ontology-based search systems can support important steps in literature reviews. • Semantic filters reduce the need to formulate complex search queries. • Semantic search results allow quick assessment of article relevance. • Semantic analysis helps users identify topics and relevant or irrelevant outliers. • The search system prototype was found to be important, accessible and suitable.