Mapping Research Trends with the CoLiRa Framework: A Computational Review of Semantic Enrichment of Tabular Data

This article proposes a novel and replicable computational methodology named CoLiRa (Computational Literature Review & Analysis) Framework to quantitatively analyze and map the evolution of a scientific field. As a multi-stage approach, the CoLiRa Framework first uses Latent Dirichlet Allocation (LDA) to identify core research topics from a body of literature. Second, it applies cluster analysis (K-Means and Multidimensional Scaling) to map the conceptual structure of the field’s key terms. Finally, it uses linear regression analysis to quantitatively assess the development trends of these topics over time. We demonstrate our proposal through a semi-systematic literature review on the semantic enrichment of tabular data, which covers studies (up to 2024) that utilize Semantic Web ontologies, Linked Data, and knowledge graphs. The analysis of this case study revealed three core research topics and found no statistically significant evidence of a shift in topic prevalence, indicating a stable research ecosystem. This work thus offers a validated computational approach for conducting literature reviews and mapping research trends.