Semantic Web And Model Driven Engineering

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.

Defining a formal domain ontology is considered a useful, not to say necessary step in almost every software project. This is because software deals with ideas rather than with self-evident physical artefacts. However, this development step is hardly ever done, as ontologies rely on well-defined and semantically powerful AI concepts such as description logics or rule-based systems, and most software engineers are unfamiliar with these. This book fills this gap by covering the subject of MDA application for ontology development on the Semantic Web. The writing is technical yet clear, and is illustrated with examples. The book is supported by a website.

Over the last decade, ontology has become an important modeling component in software engineering. Semantic Web Enabled Software Engineering presents some critical findings on opening a new direction of the research of Software Engineering, by exploiting Semantic Web technologies. Most of these findings are from selected papers from the Semantic Web Enabled Software Engineering (SWESE) series of workshops starting from 2005. Edited by two leading researchers, this advanced text presents a unifying and contemporary perspective on the field. The book integrates in one volume a unified perspective on concepts and theories of connecting Software Engineering and Semantic Web. It presents state-of-the-art techniques on how to use Semantic Web technologies in Software Engineering and introduces techniques on how to design ontologies for Software Engineering.

Defining a formal domain ontology is considered a useful, not to say necessary step in almost every software project. This is because software deals with ideas rather than with self-evident physical artefacts. However, this development step is hardly ever done, as ontologies rely on well-defined and semantically powerful AI concepts such as description logics or rule-based systems, and most software engineers are unfamiliar with these. This book fills this gap by covering the subject of MDA application for ontology development on the Semantic Web. The writing is technical yet clear, and is illustrated with examples. The book is supported by a website.

This book is part II of a two-volume work that contains the refereed proceedings of the 13th International Conference on Model Driven Engineering Languages and Systems, MODELS 2010, held in Oslo, Norway, during October 3-8, 2010. The 54 revised full papers presented were carefully reviewed and selected from 252 submissions. The papers are organized in topical sections on genericity and generalization, model migration and incremental manipulation, modeling model transformations, verifying consistency and conformance, taming modeling complexity, modeling user-system interaction, model-driven quality assurance, managing variability, multi-modeling approaches, distributed/embedded software development, (de)composition and refactoring, model change, (meta)models at runtime, requirements engineering, slicing and model transformations, incorporating quality concerns in MDD, model-driven engineering in practice, and modeling architecture.

This book constitutes thoroughly revised and selected papers from the 8th International Conference on Model-Driven Engineering and Software Development, MODELSWARD 2020, held in Valletta, Malta, in February 2020. The 15 revised and extended papers presented in this volume were carefully reviewed and selected from 66 submissions. They present recent research results and development activities in using models and model driven engineering techniques for software development. The papers are organized in topical sections on​ methodologies, processes and platforms; applications and software development; modeling languages, tools and architectures.

According to the W3C Semantic Web Activity [1]: The Semantic Web provides a common framework that allows data to be shared and reused across appli- tion, enterprise, and community boundaries. This statement clearly explains that the Semantic Web is about data sharing. Currently, the Web uses hyperlinks to connect Web pages. The Semantic Web goes beyond that and focuses on data and envisions the creation of the web of data. On the Semantic Web, anyone can say anything about any resource on the Web. This is fully based on the concept of semantic - notations, where each resource on the Web can have an assigned meaning. This is done through the use of ontologies as a formal and explicit representation of domain concepts and their relationships [2]. Ontologies are formally based on description logics. This enables agents and applications to reason over the data when searching the Web, which has not previously been possible. Web 2. 0 has gradually evolved from letting the Web users play a more active role. Unlike the initial version of the Web, where the users mainly “consumed” content, users are now offered easy-to-use services for content production and publication. Mashups, blogs, wikis, feeds, interface remixes, and social networking/tagging s- tems are examples of these well-known services. The success and wide adoption of Web 2. 0 was in its reliance on social interactions as an inevitable characteristic of the use and life of the Web. In particular, Web 2.