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Information and organization in public health institutes: an ontology-based modeling of the entities in the reception-analysis-report phases

View Article: PubMed Central - PubMed

ABSTRACT

Background: Ontologies are widely used both in the life sciences and in the management of public and private companies. Typically, the different offices in an organization develop their own models and related ontologies to capture specific tasks and goals. Although there might be an overall coordination, the use of distinct ontologies can jeopardize the integration of data across the organization since data sharing and reusability are sensitive to modeling choices.

Results: The paper provides a study of the entities that are typically found at the reception, analysis and report phases in public institutes in the life science domain. Ontological considerations and techniques are introduced and their implementation exemplified by studying the Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), a public veterinarian institute with different geographical locations and several laboratories. Different modeling issues are discussed like the identification and characterization of the main entities in these phases; the classification of the (types of) data; the clarification of the contexts and the roles of the involved entities. The study is based on a foundational ontology and shows how it can be extended to a comprehensive and coherent framework comprising the different institute’s roles, processes and data. In particular, it shows how to use notions lying at the borderline between ontology and applications, like that of knowledge object. The paper aims to help the modeler to understand the core viewpoint of the organization and to improve data transparency.

Conclusions: The study shows that the entities at play can be analyzed within a single ontological perspective allowing us to isolate a single ontological framework for the whole organization. This facilitates the development of coherent representations of the entities and related data, and fosters the use of integrated software for data management and reasoning across the company.

No MeSH data available.


Category hierarchy of the DOLCE ontology. DOLCE fragment, from [33], with an extension of the social object category (gray boxes). Arrows represent ISA relationships and dotted arrows chains of ISA
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Fig2: Category hierarchy of the DOLCE ontology. DOLCE fragment, from [33], with an extension of the social object category (gray boxes). Arrows represent ISA relationships and dotted arrows chains of ISA

Mentions: In this paper we adopt the foundational ontology DOLCE [33] with some extensions relative to the categories of roles and descriptions as presented in [34]. DOLCE is a foundational and formal ontology developed from cognitive and linguistic considerations and with particular emphasis on social reality (Fig. 2). Our choice of DOLCE relies on a few observations: DOLCE’s underlying principles and construction techniques have been well described [33] and there is evidence that this ontology is preferred even by non trained users [15], the ontology is available in different formalisms [35], it is stable and several extensions are available, e.g. social roles [36], artifacts and products [37] and mental states [38]. Furthermore, the ontology has been verified in terms of ontological and logical soundness [39, 40].Fig. 2


Information and organization in public health institutes: an ontology-based modeling of the entities in the reception-analysis-report phases
Category hierarchy of the DOLCE ontology. DOLCE fragment, from [33], with an extension of the social object category (gray boxes). Arrows represent ISA relationships and dotted arrows chains of ISA
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5017037&req=5

Fig2: Category hierarchy of the DOLCE ontology. DOLCE fragment, from [33], with an extension of the social object category (gray boxes). Arrows represent ISA relationships and dotted arrows chains of ISA
Mentions: In this paper we adopt the foundational ontology DOLCE [33] with some extensions relative to the categories of roles and descriptions as presented in [34]. DOLCE is a foundational and formal ontology developed from cognitive and linguistic considerations and with particular emphasis on social reality (Fig. 2). Our choice of DOLCE relies on a few observations: DOLCE’s underlying principles and construction techniques have been well described [33] and there is evidence that this ontology is preferred even by non trained users [15], the ontology is available in different formalisms [35], it is stable and several extensions are available, e.g. social roles [36], artifacts and products [37] and mental states [38]. Furthermore, the ontology has been verified in terms of ontological and logical soundness [39, 40].Fig. 2

View Article: PubMed Central - PubMed

ABSTRACT

Background: Ontologies are widely used both in the life sciences and in the management of public and private companies. Typically, the different offices in an organization develop their own models and related ontologies to capture specific tasks and goals. Although there might be an overall coordination, the use of distinct ontologies can jeopardize the integration of data across the organization since data sharing and reusability are sensitive to modeling choices.

Results: The paper provides a study of the entities that are typically found at the reception, analysis and report phases in public institutes in the life science domain. Ontological considerations and techniques are introduced and their implementation exemplified by studying the Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), a public veterinarian institute with different geographical locations and several laboratories. Different modeling issues are discussed like the identification and characterization of the main entities in these phases; the classification of the (types of) data; the clarification of the contexts and the roles of the involved entities. The study is based on a foundational ontology and shows how it can be extended to a comprehensive and coherent framework comprising the different institute’s roles, processes and data. In particular, it shows how to use notions lying at the borderline between ontology and applications, like that of knowledge object. The paper aims to help the modeler to understand the core viewpoint of the organization and to improve data transparency.

Conclusions: The study shows that the entities at play can be analyzed within a single ontological perspective allowing us to isolate a single ontological framework for the whole organization. This facilitates the development of coherent representations of the entities and related data, and fosters the use of integrated software for data management and reasoning across the company.

No MeSH data available.