An Ontological Framework for Multidimensional and Multivariate Data Visualization with Applications to Financial and Accounting Data

Abstract

Selecting an appropriate visualization technique for multidimensional and multivariate financial and accounting (F&A) data remains a complex, user-dependent task. The TaxUI&BV4FADA taxonomy previously organized this problem along four dimensions—visualization techniques, user intentions and analytical goals, interaction possibilities, and user groups—but as a human-readable structure, it could not be queried, validated, or integrated into semantic decision-support pipelines. This paper presents an ontological framework that extends TaxUI&BV4FADA into a machine-readable OWL DL artifact authored in WebProtégé, with OWL used for semantic structuring and SPARQL used for score-based recommendation retrieval. The framework formalizes the four taxonomy dimensions and adds a decision-support layer and an evaluation layer. An explicit F&A semantic mapping is provided, and two contrasting worked scenarios—a financial analyst testing a gross-margin hypothesis and a CFO seeking a quarterly overview—show that the framework discriminates between F&A roles and analytical tasks. The evaluation demonstrates logical consistency, competency-question satisfaction, and internal consistency of the populated recommendation matrix against taxonomy-derived expectations, rather than independent empirical recommendation accuracy. This constitutes an internal, artifact-centered validation rather than an external empirical study with end users, and a protocol for future empirical validation with financial and accounting professionals is outlined. The framework provides a domain-oriented semantic and matrix-based decision-support foundation on which executable F&A visualization recommenders can be built.