Homogenise: método para modelagem ontológica do conhecimento em pesquisas quali-quanti

Abstract

Quali-quanti analysis is a research technique that uses qualitative and quantitative data, with the need to integrate them under the same objects of study. Using different forms of representation of this data makes it possible to make more complex relationships clear and better assimilate the research information. The knowledge about the data needs to be described. Data Understanding and Preparation are initial activities in the process flow of quali-quanti research, which can use techniques to detail knowledge. In this context, Ontologies are commonly used to describe the knowledge embedded in data collections and can facilitate extracting information, disambiguating terms, and formalizing and preserving the semantics of variables. Thus: “How to understand and prepare data using ontologies to represent knowledge to improve quali-quanti research?” To answer the question, a systematic method based on semantic data annotation is proposed to explore data, information, and knowledge generated by research through graphs. A semantic data dictionarization technique generated graphs from ontologies, metadata, and data. It allowed domain experts to explore the information and knowledge investigated by explaining it from the data. Graphs organize semantically annotated data to explore and filter information to answer research interest questions. They facilitate the graphical visualization of data and have the potential to enhance quali-quanti analyses. A case study in the Psychology research area illustrates and validates the proposed method in quali-quanti research that explores psychotherapeutic data. As a contribution, it is shown how the manipulation of graphs improves quali-quanti analysis by merging quantitative and qualitative data semantically annotated and integrated into the same graph. Furthermore, the experiment with the method created a new domain ontology in psychotherapy research. Finally, when executing the method, different challenges were pointed out that specifically indicate the needs for its evolution