Abstract
Introduction: The International Classification for Nursing Practice (ICNP®) includes an ontology to represent the terms contained within it. In Brazil, in order to contribute to the development of this classification, an ontology was elaborated, comprising the representation of concepts and terms from the International Classification of Public Health Nursing Practice (Classificacao Internacional para as Praticas de Enfermagem em Saude Coletiva – CIPESC®). The identification of ontology elements in the aforementioned classification systems helps to understand how they might be used to represent the elements of nursing practice in an automated manner. Objectives: To identify ontology elements in the ICNP® and CIPESC®. Methods: Documentary, exploratory, and descriptive study. Data collection was based on the capture of structural characteristics of the various versions of the ICNP® and of the CIPESC®, including axis structure and term hierarchy structure. Data analysis was performed by comparing the elements of the captured characteristics with the following ontology elements: concepts, instances, properties, relationships, constrains and axioms. Results: The structural characteristics of ICNP® and CIPESC® are presented. Concepts, properties, relationships, constrains, and axioms were identified in both classifications. Conclusions: An ontology ensures consistency to nursing terminologies, providing evidence for practice and contributing to the unification of the nursing language. This research facilitates the development of ontologies for nursing practice based on nursing terminologies, contributing to the development of health policies by using ontologies in information systems.
Highlights
The goal of this introduction is to sketch, on an informal level, what the Semantic Web is, why it needs ontologies, and where description logics come into play
We will first give a brief introduction to description logics, and argue why they are well-suited as ontology languages
Parent ≡ Human ∃hasParent−. , Grandparent ≡ ∃hasParent−.Parent, The TBox consisting of the above concept definitions and general concept inclusion (GCI), together with the fact that hasAncestor is a transitive superrole of hasParent, implies the following subsumption relationship: Grandparent Sorcerer ∃hasParent−.∃hasParent−.Sorcerer, 2 In order to give cyclic definitions definitional impact, one would need to use fixpoint semantics for them [64, 2]
Summary
The goal of this introduction is to sketch, on an informal level, what the Semantic Web is, why it needs ontologies, and where description logics come into play. Optimized systems (FaCT, Race, and Dlp [55, 45, 68]) showed that tableau-based algorithm for expressive DLs lead to a good practical behavior of the system even on (some) large knowledge bases In this phase, the relationship to modal logics [29, 74] and to decidable fragments of first-order logic was studied in more detail [16, 66, 42, 40, 41], and applications in databases (like schema reasoning, query optimization, and DB integration) were investigated [21, 22, 25, 26]. The actual use of DLs providing these features as the underlying logical formalism of the web ontology languages OIL and DAML+OIL [36, 52] substantiates this claim [76]
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