AUTHOR=Nicholson Nicholas , Štotl Iztok 

TITLE=A generic framework for the semantic contextualization of indicators

JOURNAL=Frontiers in Computer Science

VOLUME=Volume 6 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2024.1463989

DOI=10.3389/fcomp.2024.1463989

ISSN=2624-9898

ABSTRACT=Indicators are quantitative or qualitative measures used to gauge the status of many aspects of society as well as to assess change over time (such as monitoring the progress or effectiveness of a public policy). Ideally, indicators should be precisely defined and measurements made according to harmonized procedures that may not be feasible in practice, especially in domains such as health, where indicators are often derived from pre-existing, heterogeneous datasets. Integrating such data has posed a persistent challenge, but semantic technologies offer promising solutions by enriching the data with semantic information in a relatively simple, linkable, and non-disruptive way. However, without adequate standard frameworks or guiding principles on data enrichment, the difficulties associated with data integration are unlikely to be resolved. Creating semantic relationships in an uncontrolled way may only serve to exacerbate the heterogeneity problems. In order to avert such difficulties, a concept is proposed based on the ISO/IEC 11179 metadata registry standard and the common core ontologies to provide a generic, domain-neutral indicator contextualization framework for structuring and linking distributed datasets with contextual metadata according to a standard model. The contextual information can be dereferenced using standard query tools to provide data users a comprehensive understanding and overview of the indicator. The framework is amenable to deep learning applications via the principles of semantic data models, linked open data, and knowledge organization systems.