An Ontological Characterization for SBSI Research Topics

Context: The Brazilian Information Systems (IS) community has grown in complexity involving different research topics. Characterizing the IS community research topics is vital to bringing knowledge to advance IS research maturity. Problem: The complexity, diversity, and plurality of research topics in IS present a challenging task involving collecting adequate data, analyzing, and exploring different techniques to understand the evolution in the area. Solution: Using research data published by the Brazilian Symposium on Information Systems (Simpósio Brasileiro de Sistemas de Informação - SBSI), we provide the NetO+ ontology to enable automated reasoning about SBSI research topics characterization. IS Theory: This study uses the language action perspective, highlighting what people do while communicating and how they create scientific reality using language. We focus on the SBSI research topics with automatic characterization using the Large Language Model (LLM) and ontology techniques. Method: We analyzed the technological research topics employing semantic analysis using an LLM to identify research topics with keywords related to articles’ titles. Also, the ACM taxonomy CCS Concepts in the IS branch were allied to ontological inferences. Summarization of Results: The findings with LLM to extract keywords from articles, and the ontology inference allows us to find research topics. This approach provides an overview of SBSI research, highlighting the topics’ evolution and their interconnections with ACM taxonomy. Contributions and Impacts on the IS area: This work provides IS research topics to bring knowledge enabling decision-making for sustainable community growth based on the socio-technical approach concerning excellence in IS conception, development, and maintainability.