Foundational ontologies meet ontology matching: A survey

Santos, Cássia Trojahn dos; Vieira, Renata; Schmidt, Daniela; Pease, Adam; Guizzardi, Giancarlo

doi:10.3233/sw-210447

Cited by 21 publications

(8 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Trojahn et al [ 88 ] performed a survey on the use of foundational ontologies for making domain ontologies interoperable. The work provides an overview of various ontology-matching activities that can benefit from foundational ontologies.…”

Section: Related Workmentioning

confidence: 99%

The use of foundational ontologies in biomedical research

Bernabé,

Queralt-Rosinach,

Silva Souza

et al. 2023

J Biomed Semant

View full text Add to dashboard Cite

Background The FAIR principles recommend the use of controlled vocabularies, such as ontologies, to define data and metadata concepts. Ontologies are currently modelled following different approaches, sometimes describing conflicting definitions of the same concepts, which can affect interoperability. To cope with that, prior literature suggests organising ontologies in levels, where domain specific (low-level) ontologies are grounded in domain independent high-level ontologies (i.e., foundational ontologies). In this level-based organisation, foundational ontologies work as translators of intended meaning, thus improving interoperability. Despite their considerable acceptance in biomedical research, there are very few studies testing foundational ontologies. This paper describes a systematic literature mapping that was conducted to understand how foundational ontologies are used in biomedical research and to find empirical evidence supporting their claimed (dis)advantages. Results From a set of 79 selected papers, we identified that foundational ontologies are used for several purposes: ontology construction, repair, mapping, and ontology-based data analysis. Foundational ontologies are claimed to improve interoperability, enhance reasoning, speed up ontology development and facilitate maintainability. The complexity of using foundational ontologies is the most commonly cited downside. Despite being used for several purposes, there were hardly any experiments (1 paper) testing the claims for or against the use of foundational ontologies. In the subset of 49 papers that describe the development of an ontology, it was observed a low adherence to ontology construction (16 papers) and ontology evaluation formal methods (4 papers). Conclusion Our findings have two main implications. First, the lack of empirical evidence about the use of foundational ontologies indicates a need for evaluating the use of such artefacts in biomedical research. Second, the low adherence to formal methods illustrates how the field could benefit from a more systematic approach when dealing with the development and evaluation of ontologies. The understanding of how foundational ontologies are used in the biomedical field can drive future research towards the improvement of ontologies and, consequently, data FAIRness. The adoption of formal methods can impact the quality and sustainability of ontologies, and reusing these methods from other fields is encouraged.

show abstract

Section: Related Workmentioning

confidence: 99%

The use of foundational ontologies in biomedical research

Bernabé,

Queralt-Rosinach,

Silva Souza

et al. 2023

J Biomed Semant

View full text Add to dashboard Cite

show abstract

“…Similarly, having a large catalog with domain data related to top-level data may significantly improve tasks in which schemes must be matched according to a reference standard [23]. It can also provide training data for ontology matching tasks leveraging on data annotated with categories coming from foundational ontologies-a still unexplored approach to ontology matching [24]. Finally, our catalog can be exploited as a training set for machine learning prediction tasks, where the goal is to predict the correct foundational category of a given class, thus providing automated support to build new models and define their scope.…”

Section: Relevance For Researchmentioning

confidence: 99%

A FAIR Model Catalog for Ontology-Driven Conceptual Modeling Research

Barcelos

Sales

Fumagalli

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Conceptual models are artifacts representing conceptualizations of particular domains. Hence, multi-domain model catalogs serve as empirical sources of knowledge and insights about specific domains, about the use of a modeling language's constructs, as well as about the patterns and anti-patterns recurrent in the models of that language crosscutting different domains. However, to support domain and language learning, model reuse, knowledge discovery for humans, and reliable automated processing and analysis by machines, these catalogs must be built following generally accepted quality requirements for scientific data management. Especially, all scientific (meta)data-including models-should be created using the FAIR principles (Findability, Accessibility, Interoperability, and Reusability). In this paper, we report on the construction of a FAIR model catalog for Ontology-Driven Conceptual Modeling research, a trending paradigm lying at the intersection of conceptual modeling and ontology engineering in which the Unified Foundational Ontology (UFO) and OntoUML emerged among the most adopted technologies. In this initial release, the catalog includes over a hundred models, developed in a variety of contexts and domains. The paper also discusses the research implications for (ontology-driven) conceptual modeling of such a resource.

show abstract

“…It includes dozens of domains ontologies, contains roughly 20,000 terms and 80,000 logical statements and it is largely used for translations to languages and mappings to WordNet [44]. We suggest [49] for a deeper overview of existing foundational ontologies and how they are used across several computer-based tasks.…”

Section: Domain and Top-level Ontology Analysismentioning

confidence: 99%

Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines

et al. 2022

View full text Add to dashboard Cite

The World Health Organization and the American College of Sports Medicine have released guidelines on physical activity and sedentary behavior, as part of an effort to reduce inactivity worldwide. However, to date, there is no computational model that can facilitate the integration of these recommendations into health solutions (e.g., digital coaches). In this paper, we present an operational and machine-readable model that represents and is able to reason about these guidelines. To this end, we adopted a symbolic AI approach that combines two paradigms of research in knowledge representation and reasoning: ontology and rules. Thus, we first present HeLiFit, a domain ontology implemented in OWL, which models the main entities that characterize the definition of physical activity, as defined per guidance. Then, we describe HeLiFit-Rule, a set of rules implemented in the RDFox Rule language, which can be used to represent and reason with these recommendations in concrete real-world applications. Furthermore, to ensure a high level of syntactic/semantic interoperability across different systems, our framework is also compliant with the FHIR standard. Through motivating scenarios that highlight the need for such an implementation, we finally present an evaluation of our model that provides results that are both encouraging in terms of the value of our solution and also provide a basis for future work.

show abstract

Foundational ontologies meet ontology matching: A survey

Cited by 21 publications

References 58 publications

The use of foundational ontologies in biomedical research

The use of foundational ontologies in biomedical research

A FAIR Model Catalog for Ontology-Driven Conceptual Modeling Research

Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines

Contact Info

Product

Resources

About