Ontological realism: A methodology for coordinated evolution of scientific ontologies

Smith, Barry; Ceusters, Werner

doi:10.3233/ao-2010-0079

Cited by 200 publications

(130 citation statements)

References 85 publications

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“…BFO’s realist methodology [19] implies that all instances should be actual entities. Thus, one cannot represent directly such a possible-but-not-actual condition in an ontology based on BFO.…”

Section: Resultsmentioning

confidence: 99%

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An ontological analysis of medical Bayesian indicators of performance

et al. 2017

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BackgroundBiomedical ontologies aim at providing the most exhaustive and rigorous representation of reality as described by biomedical sciences. A large part of medical reasoning deals with diagnosis and is essentially probabilistic. It would be an asset for biomedical ontologies to be able to support such a probabilistic reasoning and formalize Bayesian indicators of performance: sensitivity, specificity, positive predictive value and negative predictive value. In doing so, one has to consider that not only the positive and negative predictive values, but also sensitivity and specificity depend upon the group under consideration: this is the “spectrum effect”.MethodsThe sensitivity value of an index test IT for a disease M in a group g is identified with the proportion of people in g who have M who would get a positive result to IT if the test IT was realized on them. This value can be estimated by selecting a reference test RT for M and a sample s of g, and measuring the proportion, among members of s having a positive result to RT, of those who got a positive result to IT. Similar approximation strategies hold for prevalence, specificity, PPV and NPV. Indicators of diagnostic performances and their estimations are formalized in the context of the OBO Foundry, built on the realist upper ontology Basic Formal Ontology (BFO).ResultsEntities and relations from the Ontology for Biomedical investigations (OBI) and the Information Artifact Ontology (IAO) are used and complemented to represent reference tests and index tests, tests executions, tests results and the relations involving those entities, as well as the values of indicators of performance and their estimates. The computations taking as input several estimates of an indicator of performance to produce a finer estimate are also represented. The value of e.g. sensitivity estimates should be dissociated from the real sensitivity value – which involves possible, non-actual conditions, namely the result a person would get if a medical test would be performed on her. Such conditions could not be directly represented in a realist ontology, but a representation is proposed that introduces only actual entities by considering a disposition whose probability value is the real sensitivity value. A sensitivity estimate is a data item which is about such a disposition.ConclusionsThis model provides theoretical basis for the representation of entities supporting Bayesian reasoning in ontologies.

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Section: Resultsmentioning

confidence: 99%

“…Therefore, the classes IT 0 and RT 0 , which mention ‘detecting albuminuria’ without specifying a cutoff value, are not scientifically defined: those classes are not universals, but rather collection of particulars [19] whose nature is partly social ([8] acknowledge this limitation in their meta-analysis).…”

Section: Resultsmentioning

confidence: 99%

An ontological analysis of medical Bayesian indicators of performance

et al. 2017

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“…, vaccines, vaccine ingredients, vaccination methods, and host immune responses) by representing these types of data and the relations among them. The universal (or called class) level representation also provides a framework of representing particular (or instance) level data [56]. An example of ontology-supported data integration is that the Vaccine Ontology (VO) [57,58] has been used to integrate data available from the VIOLIN vaccine database [46].…”

Section: A Promising Solution: Ontology-based Data Miningmentioning

confidence: 99%

Ontology-supported research on vaccine efficacy, safety and integrative biological networks

2014

Expert Review of Vaccines

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Summary While vaccine efficacy and safety research has dramatically progressed with the methods of in silico prediction and data mining, many challenges still exist. A formal ontology is a human- and computer-interpretable set of terms and relations that represent entities in a specific domain and how these terms relate to each other. Several community-based ontologies (including the Vaccine Ontology, Ontology of Adverse Events, and Ontology of Vaccine Adverse Events) have been developed to support vaccine and adverse event representation, classification, data integration, literature mining of host-vaccine interaction networks, and analysis of vaccine adverse events. The author further proposes minimal vaccine information standards and their ontology representations, ontology-based linked open vaccine data and meta-analysis, an integrative One Network (“OneNet”) Theory of Life, and ontology-based approaches to study and apply the OneNet theory. In the Big Data era, these proposed strategies provide a novel framework for advanced data integration and analysis of fundamental biological networks including vaccine immune mechanisms.

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“…We also adhere to the principles of ontological realism [24]. OAE is aligned with the Basic Formal Ontology (BFO) (http://www.ifomis.org/bfo) version 2.0.…”

Section: Introductionmentioning

confidence: 99%

OAE: The Ontology of Adverse Events

et al. 2014

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BackgroundA medical intervention is a medical procedure or application intended to relieve or prevent illness or injury. Examples of medical interventions include vaccination and drug administration. After a medical intervention, adverse events (AEs) may occur which lie outside the intended consequences of the intervention. The representation and analysis of AEs are critical to the improvement of public health.DescriptionThe Ontology of Adverse Events (OAE), previously named Adverse Event Ontology (AEO), is a community-driven ontology developed to standardize and integrate data relating to AEs arising subsequent to medical interventions, as well as to support computer-assisted reasoning. OAE has over 3,000 terms with unique identifiers, including terms imported from existing ontologies and more than 1,800 OAE-specific terms. In OAE, the term ‘adverse event’ denotes a pathological bodily process in a patient that occurs after a medical intervention. Causal adverse events are defined by OAE as those events that are causal consequences of a medical intervention. OAE represents various adverse events based on patient anatomic regions and clinical outcomes, including symptoms, signs, and abnormal processes. OAE has been used in the analysis of several different sorts of vaccine and drug adverse event data. For example, using the data extracted from the Vaccine Adverse Event Reporting System (VAERS), OAE was used to analyse vaccine adverse events associated with the administrations of different types of influenza vaccines. OAE has also been used to represent and classify the vaccine adverse events cited in package inserts of FDA-licensed human vaccines in the USA.ConclusionOAE is a biomedical ontology that logically defines and classifies various adverse events occurring after medical interventions. OAE has successfully been applied in several adverse event studies. The OAE ontological framework provides a platform for systematic representation and analysis of adverse events and of the factors (e.g., vaccinee age) important for determining their clinical outcomes.

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Ontological realism: A methodology for coordinated evolution of scientific ontologies

Cited by 200 publications

References 85 publications

An ontological analysis of medical Bayesian indicators of performance

An ontological analysis of medical Bayesian indicators of performance

Ontology-supported research on vaccine efficacy, safety and integrative biological networks

OAE: The Ontology of Adverse Events

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