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The confluence of Artificial Intelligence of Things (AIoT) and Semantic Web technologies is nothing short of revolutionary. The profound impact of this synergy extends far beyond the realms of industry, research, and society; it shapes the very fabric of our future. Semantic Web Technologies and Applications in Artificial Intelligence of Things is a meticulously crafted reference that not only acknowledges this significance but also serves as a guide for those navigating the complexities of Industry 4.0 and AIoT. This curated compendium of cutting-edge technologies acts as a veritable knowledge base for future developments. As academics, scholars, and industry professionals, the ideal audience of this book, will find meticulously curated content that caters to their diverse interests and expertise, covering topics ranging from smart agriculture, manufacturing, industry, health sciences, and government. Seasoned academics, students, and visionary industry leaders, will find this book to be an indispensable guide that paves the way for innovation and progress.
In the last few years, there has been an increased advancement and evolution in semantic web and information systems in a variety of fields. The integration of these approaches to ontology engineering, sophisticated methods and algorithms for open linked data extraction, and advanced decision-making creates new opportunities for a bright future. Innovations, Developments, and Applications of Semantic Web and Information Systems is a critical scholarly resource that discusses integrated methods of research and analytics in information technology. Featuring coverage on a broad range of topics, such as cognitive computing, artificial intelligence, machine learning, data analysis, and algorithms, this book is geared towards researchers, academicians, and professionals seeking current information on semantic web and information systems.
Continual advancements in web technology have highlighted the need for formatted systems that computers can utilize to easily read and sift through the hundreds of thousands of data points across the internet. Therefore, having the most relevant data in the least amount of time to optimize the productivity of users becomes a priority. Semantic Web Science and Real-World Applications provides emerging research exploring the theoretical and practical aspects of semantic web science and real-world applications within the area of big data. Featuring coverage on a broad range of topics such as artificial intelligence, social media monitoring, and microblogging recommendation systems, this book is ideally designed for IT consultants, academics, professionals, and researchers of web science seeking the current developments, requirements and standards, and technology spaces presented across academia and industries.
With the advancements of semantic web, ontology has become the crucial mechanism for representing concepts in various domains. For research and dispersal of customized healthcare services, a major challenge is to efficiently retrieve and analyze individual patient data from a large volume of heterogeneous data over a long time span. This requirement demands effective ontology-based information retrieval approaches for clinical information systems so that the pertinent information can be mined from large amount of distributed data. This unique and groundbreaking book highlights the key advances in ontology-based information retrieval techniques being applied in the healthcare domain and covers the following areas: Semantic data integration in e-health care systems Keyword-based medical information retrieval Ontology-based query retrieval support for e-health implementation Ontologies as a database management system technology for medical information retrieval Information integration using contextual knowledge and ontology merging Collaborative ontology-based information indexing and retrieval in health informatics An ontology-based text mining framework for vulnerability assessment in health and social care An ontology-based multi-agent system for matchmaking patient healthcare monitoring A multi-agent system for querying heterogeneous data sources with ontologies for reducing cost of customized healthcare systems A methodology for ontology based multi agent systems development Ontology based systems for clinical systems: validity, ethics and regulation
The Semantic Web combines the descriptive languages RDF (Resource Description Framework) and OWL (Web Ontology Language), with the data-centric, customizable XML (eXtensible Mark-up Language) to provide descriptions of the content of Web documents. These machine-interpretable descriptions allow more intelligent software systems to be written, automating the analysis and exploitation of web-based information. Software agents will be able to create automatically new services from already published services, with potentially huge implications for models of e-Business. Semantic Web Technologies provides a comprehensive overview of key semantic knowledge technologies and research. The authors explain (semi-)automatic ontology generation and metadata extraction in depth, along with ontology management and mediation. Further chapters examine how Semantic Web technology is being applied in knowledge management (“Semantic Information Access”) and in the next generation of Web services. Semantic Web Technologies: Provides a comprehensive exposition of the state-of-the art in Semantic Web research and key technologies. Explains the use of ontologies and metadata to achieve machine-interpretability. Describes methods for ontology learning and metadata generation. Discusses ontology management and evolution, covering ontology change detection and propagation, ontology dependency and mediation. Illustrates the theoretical concepts with three case studies on industrial applications in digital libraries, the legal sector and the telecommunication industry. Graduate and advanced undergraduate students, academic and industrial researchers in the field will all find Semantic Web Technologies an essential guide to the technologies of the Semantic Web.
Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL, Second Edition, discusses the capabilities of Semantic Web modeling languages, such as RDFS (Resource Description Framework Schema) and OWL (Web Ontology Language). Organized into 16 chapters, the book provides examples to illustrate the use of Semantic Web technologies in solving common modeling problems. It uses the life and works of William Shakespeare to demonstrate some of the most basic capabilities of the Semantic Web. The book first provides an overview of the Semantic Web and aspects of the Web. It then discusses semantic modeling and how it can support the development from chaotic information gathering to one characterized by information sharing, cooperation, and collaboration. It also explains the use of RDF to implement the Semantic Web by allowing information to be distributed over the Web, along with the use of SPARQL to access RDF data. Moreover, the reader is introduced to components that make up a Semantic Web deployment and how they fit together, the concept of inferencing in the Semantic Web, and how RDFS differs from other schema languages. Finally, the book considers the use of SKOS (Simple Knowledge Organization System) to manage vocabularies by taking advantage of the inferencing structure of RDFS-Plus. This book is intended for the working ontologist who is trying to create a domain model on the Semantic Web. - Updated with the latest developments and advances in Semantic Web technologies for organizing, querying, and processing information, including SPARQL, RDF and RDFS, OWL 2.0, and SKOS - Detailed information on the ontologies used in today's key web applications, including ecommerce, social networking, data mining, using government data, and more - Even more illustrative examples and case studies that demonstrate what semantic technologies are and how they work together to solve real-world problems
"The book provides a sound theoretical foundation for the application of semantic methods, concepts, technologies for practical problem solving offering original research on advanced concepts, methods, algorithms, technologies, and applications of semantic computing in real-world situations"--
Managing Trade-Offs in Adaptable Software Architectures explores the latest research on adapting large complex systems to changing requirements. To be able to adapt a system, engineers must evaluate different quality attributes, including trade-offs to balance functional and quality requirements to maintain a well-functioning system throughout the lifetime of the system. This comprehensive resource brings together research focusing on how to manage trade-offs and architect adaptive systems in different business contexts. It presents state-of-the-art techniques, methodologies, tools, best practices, and guidelines for developing adaptive systems, and offers guidance for future software engineering research and practice. Each contributed chapter considers the practical application of the topic through case studies, experiments, empirical validation, or systematic comparisons with other approaches already in practice. Topics of interest include, but are not limited to, how to architect a system for adaptability, software architecture for self-adaptive systems, understanding and balancing the trade-offs involved, architectural patterns for self-adaptive systems, how quality attributes are exhibited by the architecture of the system, how to connect the quality of a software architecture to system architecture or other system considerations, and more. - Explains software architectural processes and metrics supporting highly adaptive and complex engineering - Covers validation, verification, security, and quality assurance in system design - Discusses domain-specific software engineering issues for cloud-based, mobile, context-sensitive, cyber-physical, ultra-large-scale/internet-scale systems, mash-up, and autonomic systems - Includes practical case studies of complex, adaptive, and context-critical systems
Social Networks and the Semantic Web offers valuable information to practitioners developing social-semantic software for the Web. It provides two major case studies. The first case study shows the possibilities of tracking a research community over the Web. It reveals how social network mining from the web plays an important role for obtaining large scale, dynamic network data beyond the possibilities of survey methods. The second case study highlights the role of the social context in user-generated classifications in content, such as the tagging systems known as folksonomies.
This book is focused on an emerging area, i.e. combination of IoT and semantic technologies, which should enable breaking the silos of local and/or domain-specific IoT deployments. Taking into account the way that IoT ecosystems are realized, several challenges can be identified. Among them of definite importance are (this list is, obviously, not exhaustive): (i) How to provide common representation and/or shared understanding of data that will enable analysis across (systematically growing) ecosystems? (ii) How to build ecosystems based on data flows? (iii) How to track data provenance? (iv) How to ensure/manage trust? (v) How to search for things/data within ecosystems? (vi) How to store data and assure its quality? Semantic technologies are often considered among the possible ways of addressing these (and other, related) questions. More precisely, in academic research and in industrial practice, semantic technologies materialize in the following contexts (this list is, also, not exhaustive, but indicates the breadth of scope of semantic technology usability): (i) representation of artefacts in IoT ecosystems and IoT networks, (ii) providing interoperability between heterogeneous IoT artefacts, (ii) representation of provenance information, enabling provenance tracking, trust establishment, and quality assessment, (iv) semantic search, enabling flexible access to data originating in different places across the ecosystem, (v) flexible storage of heterogeneous data. Finally, Semantic Web, Web of Things, and Linked Open Data are architectural paradigms, with which the aforementioned solutions are to be integrated, to provide production-ready deployments.