Overview
Data provide the factual basis from which a large part of human knowledge draws. The rapid development of large language models demonstrates the significant potential of combining data. However, understanding their limitations and biases is essential to avoid errors and misleading conclusions that, in the worst cases, can lead to life-threatening decisions, particularly in healthcare.
AI technologies could help address these challenges. However, despite recent advances, Artificial Intelligence is still unable to make consistent semantic linkages. To address this, the JRC is working on a standardised approach to ensure that data are contextualised consistently and effectively.
What the JRC is doing
The JRC has developed a standardised framework for contextualising data. It aims to make data easier to interpret and combine by ensuring that the information needed to understand them is consistently captured and linked.
A standard framework would also make it easier to integrate datasets from different domains, while ensuring that data are combined in a meaningful and reliable way.
JRC work initially focused on indicators: it explored how to provide indicators with the relevant contextual information needed to interpret and use them correctly. The same approach can also be applied to data, although contextualising indicators is more complex due to the data-processing steps required to derive them.
About SOLICIT
The framework is called SOLICIT (Semantic Ontology-labelled Indicator/Data Integrative Taxonomy), a prototype of a possible standard framework for data contextualisation.
SOLICIT combines approaches from two related fields: metadata and knowledge bases (ontologies). It uses the ISO/IEC 11179 metadata registry standard for its metadata model and the common core ontologies developed in the Web Ontology Language (OWL) to define semantic relationships.
SOLICIT is designed as a hierarchy of ontologies, allowing lower-level ontologies to incorporate increasingly domain-specific concepts. This makes the framework more scalable while promoting the reuse of metadata terms and ontology classes.
Laying the groundwork for further development
Data contextualisation is essential for making data easier to interpret and combine, yet the field has not received the attention it requires. SOLICIT is an early contribution by the JRC towards developing suitable standard frameworks that could eventually be adopted across different data domains.
Policy background
- The European Commission’s Healthier together – EU non-communicable diseases (NCD) initiative.
- JRC is developing a concept to underpin a sustainable health information framework for the collection and reporting of indicators for non-communicable diseases. The framework is called CHIEF (Collaborative Health Information European Framework).
- SOLICIT is proposed for as the framework for contextualising the indicators within CHIEF.
Related publications
Nicholson N and Štotl I. (2024) A generic framework for the semantic contextualization of indicators. Front. Comput. Sci. 6:1463989. doi: 10.3389/fcomp.2024.1463989.
Nicholson N, Negrao Carvalho R, Štotl I. A FAIR Perspective on Data Quality Frameworks. Data 2025, 10, 136. doi: 10.3390/data10090136.
Why data needs context and how to achieve it in practice. Scitube. Jul 2026.