Foundational ontologies, <scp>ontology‐driven</scp> conceptual modeling, and their multiple benefits to data mining

Amaral, Glenda C. M.; Baião, Fernanda; Guizzardi, Giancarlo

doi:10.1002/widm.1408

Cited by 11 publications

(11 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, researchers can investigate whether XAI algorithms perform differently if trained on data that is organised accordingly to a model grounded on a foundational ontology, when compared to the ones trained on unstructured data. Similar investigations are discussed by Amaral, Baião & Guizzardi [ 6 ] in their paper about the use of foundational ontologies for data mining. The authors argue that the quality of data mining results is related to the extent that they accurately reflect the real world, and add that the “fundamental ontological distinctions embodied in a foundational ontology can be used to improve the quality of the data mining process, mainly when it includes information from multiple sources that may commit to different theories about a particular concept.”…”

Section: Discussionsupporting

confidence: 67%

“…Foundational ontologies are high-level, domain-independent ontologies constructed to provide basic categories and relations to concepts in domain-specific ontologies [ 4 ]. Theoretically, foundational ontologies are claimed to enhance the quality of domain specific ontologies and facilitate the interoperability among ontologies grounded on the same foundational one [ 3 – 6 ]. However, it is difficult to find empirical evidence testing these claims.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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: Discussionsupporting

confidence: 67%

Section: Introductionmentioning

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

“…Besides the basic sortal concept and relational identity, further concepts from the philosophical domain will be included in the ontology, e.g., phased sortals, perdurants, and rigidity. All these efforts will also rely on two upper-level ontologies central for us: UFO [11] and BFO [12].…”

Section: Discussionmentioning

confidence: 99%

The Sortal Concept in the Context of Biomedical Record Linkage

Miletić¹,

Sariyar²

2022

Studies in Health Technology and Informatics

View full text Add to dashboard Cite

Biomedical Record Linkage is especially designed for linking data of patients in different data repositories. An important question in this context is whether singling-out is sufficient for identifying a patient, and if not, what is in general required for identification. To provide hints for an answer, we will extend previous works on the concept of identity and extend the sortal concept, stemming from analytical philosophy and upper-level ontologies. A sortal is a concept that is associated with an identity criterion. For example, the concept “set” has the identity criterion “having the same members”. Based on a description of a record linkage setting, we operationalize the sortal concept by providing a distinction between the digital representation of a person (d-sortal) and the person in flesh (b-sortal).

show abstract

“…P. K. Sinha et al provided a systematic review of the most significant DM ontologies [7]. G. Amaral et al summarized multiple benefits of ontologies to DM [8]. All the mentioned fundamental works and elaborate reviews form a consensus about high relevance, high demand, and wide prospects of semantic DM.…”

Section: Related Workmentioning

confidence: 99%

Ontology-Driven Visual Analytics Platform for Semantic Data Mining and Fuzzy Classification

Ryabinin

Chumakov

Белоусов

et al. 2022

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

Visualization is claimed as one of the essential “V’s” of Big Data since it allows presenting the data in a human-friendly way and is, therefore, a stepping-stone for the Big Data mining process. Visual analytics, in turn, ensures knowledge discovery out of the data through cognitive graphics and filtering capabilities. But to be efficient, visualization and analytics tools have to consider other Big Data “V’s” by handling the large data volumes, keeping up with the data growth and changing velocity, and adapting to the variety of the data representation formats. We propose using ontology engineering methods to create a visual analytics platform controlled by an ontological knowledge base that describes supported data types, input formats, data filters, visual objects, and visualization algorithms, as well as available communication protocols and computing nodes, the platform modules can run on. This allows introducing new functions and distributed computation scenarios to the platform on the fly just by extending the underlying domain ontologies without changing the source code of the platform’s core. The analytics flow inside this platform is described by task ontologies enabling semantic data mining process. As a result, seamless integration with different data sources is achieved, including plain files, databases, and even third-party soft- and hardware solvers. We demonstrate the viability of the approach proposed by solving several data mining and fuzzy classification problems, including the assessment of the citizens’ regional identity according to the mental maps they draw and the reconstruction of ontogenesis of extinct synapsid ‘Titanophoneus potens’ Efremov, 1938.

show abstract

Foundational ontologies, ontology‐driven conceptual modeling, and their multiple benefits to data mining

Cited by 11 publications

References 42 publications

The use of foundational ontologies in biomedical research

The use of foundational ontologies in biomedical research

The Sortal Concept in the Context of Biomedical Record Linkage

Ontology-Driven Visual Analytics Platform for Semantic Data Mining and Fuzzy Classification

Contact Info

Product

Resources

About