Summary of Ssdont: An Ontology For Representing Single-subject Design Studies, by Idoia Berges et al.
SSDOnt: an Ontology for representing Single-Subject Design Studies
by Idoia Berges, Jesús Bermúdez, Arantza Illarramendi
First submitted to arxiv on: 26 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed ontology, SSDOnt, aims to standardize the description and annotation of Single-Subject Design (SSD) studies in education and biomedicine. By providing a formal vocabulary for annotating SSD configurations and results, the ontology enables more accurate querying and searching of these study designs. The methodology employed was NeOn, which led to a Description Logic-based model implemented in OWL 2 DL. The resulting reference model features a suitable terminology for annotating SSD studies, including phases, intervention types, outcomes, and results. Examples of queries that can be posed to the ontology are provided, showcasing its potential for precision in describing specific interventions and outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SSDOnt is an important tool that helps researchers find and understand single-subject design studies. This special type of study is used in fields like education and medicine. Right now, finding these studies is difficult because there’s no standard way to describe them. SSDOnt changes this by providing a set of rules and terms that can be used to describe and search for SSD studies. This makes it easier for researchers to ask complex questions about the studies and find answers quickly. |
Keywords
* Artificial intelligence * Precision
