Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques
Not on the map. Why?
Abstract (arXiv)
Background: Adverse Childhood Experiences (ACEs), a set of negative events and processes that a person might encounter during childhood and adolescence, have been proven to be linked to increased risks of a multitude of negative health outcomes and conditions when children reach adulthood and beyond. Objective: To better understand the relationship between ACEs and their relevant risk factors with associated health outcomes and to eventually design and implement preventive interventions, access to an integrated coherent dataset is needed. Therefore, we implemented a formal ontology as a resource to allow the mental health community to facilitate data integration and knowledge modeling and to improve ACEs surveillance and research. Methods: We use advanced knowledge representation and Semantic Web tools and techniques to implement the ontology. The current implementation of the ontology is expressed in the description logic ALCRIQ(D), a sublogic of Web Ontology Language (OWL 2). Results: The ACEs Ontology has been implemented and made available to the mental health community and the public via the BioPortal repository. Moreover, multiple use-case scenarios have been introduced to showcase and evaluate the usability of the ontology in action. The ontology was created to be used by major actors in the ACEs community with different applications, from the diagnosis of individuals and predicting potential negative outcomes that they might encounter to the prevention of ACEs in a population and designing interventions and policies. Conclusions: The ACEs Ontology provides a uniform and reusable semantic network and an integrated knowledge structure for mental health practitioners and researchers to improve ACEs surveillance and evaluation.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Nov 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 3; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
4 papers.
- 2022An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Modelcited by 1
- 2022Adverse Childhood Experiences Identification from Clinical Notes with Ontologies and NLPcited by 1
- 2022Ontology-Driven Self-Supervision for Adverse Childhood Experiences Identification Using Social Media Datasetscited by 1
- 2026Ontology-driven personalized information retrieval for XML documentscited by 0
- 2026Ontology-driven personalized information retrieval for XML documents22 Mar 2026
- 2022Ontology-Driven Self-Supervision for Adverse Childhood Experiences Identification Using Social Media Datasets24 Aug 2022
- 2022Adverse Childhood Experiences Identification from Clinical Notes with Ontologies and NLP24 Aug 2022
- 2022An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model26 Jul 2022