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

This article introduces the CoLiRa (Computational Literature Review & Analysis) framework, a novel integration of established computational algorithms designed to quantitatively analyze and map the evolution of scientific fields. Employing a human-in-the-loop epistemological approach, CoLiRa combines the scalability of automated algorithms with the semantic coherence of expert-driven qualitative research. The multi-stage pipeline incorporates Latent Dirichlet Allocation (LDA) for thematic discovery, cluster analysis (K-Means and Multidimensional Scaling) for conceptual mapping, and Ordinary Least Squares (OLS) regression to monitor temporal trends. Algorithmic outputs are structurally validated by domain experts using quantitative metrics. The framework’s end-to-end capabilities are demonstrated through a proof-of-concept case study on the semantic enrichment of tabular data, encompassing studies up to 2024 that utilize Semantic Web ontologies, Linked Data, and knowledge graphs. The analysis identifies three core research topics and finds no statistically significant linear trends, suggesting thematic coexistence. This work provides a validated, hybrid computational approach for conducting robust literature reviews and mapping research trajectories.