Integration of the Semantic Web and Model-Driven Software
Fernando Silva Parreiras
Language: English
Pages: 249
ISBN: 1118004175
Format: PDF / Kindle (mobi) / ePub
The next enterprise computing era will rely on the synergy between both technologies: semantic web and model-driven software development (MDSD). The semantic web organizes system knowledge in conceptual domains according to its meaning. It addresses various enterprise computing needs by identifying, abstracting and rationalizing commonalities, and checking for inconsistencies across system specifications. On the other side, model-driven software development is closing the gap among business requirements, designs and executables by using domain-specific languages with custom-built syntax and semantics. It focuses on using modeling languages as programming languages.
Among many areas of application, we highlight the area of configuration management. Consider the example of a telecommunication company, where managing the multiple configurations of network devices (routers, hubs, modems, etc.) is crucial. Enterprise systems identify and document the functional and physical characteristics of network devices, and control changes to those characteristics. Applying the integration of semantic web and model-driven software development allows for
(1) explicitly specifying configurations of network devices with tailor-made languages,
(2) for checking the consistency of these specifications
(3) for defining a vocabulary to share device specifications across enterprise systems. By managing configurations with consistent and explicit concepts, we reduce cost and risk, and enhance agility in response to new requirements in the telecommunication area.
This book examines the synergy between semantic web and model-driven software development. It brings together advances from disciplines like ontologies, description logics, domain-specific modeling, model transformation and ontology engineering to take enterprise computing to the next level.
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Literal Ն≤ 1 DPE DT ≡ DR 26 CHAPTER 3 TABLE 3.4 ONTOLOGY FOUNDATIONS Syntax of Assertions. OWL 2 Syntax SameIndividual(a1 ... an) DifferentIndividuals(a1 ... an) ClassAssertion(CE a) ObjectPropertyAssertion(OPE a1 a2) NegativeObjectPropertyAssertion(OPE a1 a2) DataPropertyAssertion(DPE a lt) NegativeDataPropertyAssertion(DPE a lt) TABLE 3.5 Description Logic Syntax a1 Ԉ ... Ԉ an a1 ≠ ... ≠ an CE(a) OPE(a1, a2) ¬OPE(a1, a2) DPE(a1, lt) ¬DPE(a1, lt) Syntax of Class Expressions. OWL 2.
Analysis of ontological technical spaces and MOF technical space, explaining the features of the different paradigms. We analyze their similarities and describe frequently used patterns for transformations between instantiations of metamodeling technical spaces and ontological technical spaces.1 4.1 INTRODUCTION Ontology technologies and model-driven engineering have distinct foci. For example, MOF targets automating the management and interchange of metadata, whereas knowledge representation.
Understanding of the domain in the form of a feature model. In Section 4.4, the model categorizing related approaches is applied. 4.2 SIMILARITIES BETWEEN OWL MODELING AND UML CLASS-BASED MODELING Despite having distinct purposes, OTS and MMTS share similar constructs. Recent approaches presented similarities between MOF and RDF [53], between OWL/RDF and Object-Oriented Languages [92], and between UML and OWL [114, 42]. The features are summarized in Table 4.1. For the subtleties, please refer.
Translation rules can be written as follows: t : InBook (?x ) ∧ t : month(?x, ?m) ∧ t : title(?x, ?n) ∧ t : pages(?x, ?p) ← (s : InBook (?x ) ∨ s : Chapter (?x )) ∧ s : month(?x, ?y) ∧ builtin : notShortened (?y, ?m) ∧ s : title(?x, ?z ) ∧ builtin : toUpper (?z, ?n) ∧ s : pages(?x, ?w) ∧ s : startP age(?w, ?a ) ∧ s : endPage(?w, ?e) ∧ builtin : − (?e, ?a, ?p). (11.1) The translation rule of authors is not trivial either. While in ontology #101 the authors are collected by recursively matching.
For specifying distinct aspects of software systems. UML provides means to express dynamic behavior, whereas OWL does not. OWL is capable of inferring generalization and specialization between classes as well as class membership of objects based on the constraints imposed on the properties of class definitions, whereas UML class diagrams do not allow for dynamic specialization/generalization of classes and class memberships or any other kind of inference per se. Though schemas [111] and UML.