# Semantic web technologies in sensor-based personal health monitoring systems: A systematic mapping study

arXiv: https://arxiv.org/abs/2306.04335 · HTML: https://mlsanity.com/paper/2306.04335/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 158 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 12 of 12)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support](https://mlsanity.com/paper/2302.12389/) (2023)
- [SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators](https://mlsanity.com/paper/1805.09979/) (2018)
- [A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future](https://mlsanity.com/paper/2002.08627/) (2020)
- [Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph](https://mlsanity.com/paper/1910.01116/) (2019)
- [Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy](https://mlsanity.com/paper/2008.10733/) (2020)
- [Using a Personal Health Library-Enabled mHealth Recommender System for Self-Management of Diabetes Among Underserved Populations: Use Case for Knowledge Graphs and Linked Data](https://mlsanity.com/paper/2103.09311/) (2021)
- [Landscaping Systematic Mapping Studies in Software Engineering: A Tertiary Study](https://mlsanity.com/paper/1909.10751/) (2019)
- [Semantic Web in Healthcare: A Systematic Literature Review of Application, Research Gap, and Future Research Avenues](https://mlsanity.com/paper/2211.00058/) (2022)

## Abstract (arXiv)

In recent years, there has been an increased focus on early detection, prevention, and prediction of diseases. This, together with advances in sensor technology and the Internet of Things, has led to accelerated efforts in the development of personal health monitoring systems. This study analyses the state of the art in the use of Semantic Web technologies in sensor-based personal health monitoring systems. Using a systematic approach, a total of 48 systems are selected as representative of the current state of the art. We critically analyse the extent to which the selected systems address seven key challenges: interoperability, situation detection, situation prediction, decision support, context awareness, explainability, and uncertainty handling. We discuss the role and limitations of Semantic Web technologies in managing each challenge. We then conduct a quality assessment of the selected systems based on the data and devices used, system and components development, rigour of evaluation, and accessibility of research outputs. Finally, we propose a reference architecture to provide guidance for the design and development of new systems. This study provides a comprehensive mapping of the field, identifies inadequacies in the state of the art, and provides recommendations for future research.