Modeling With Rules Using Semantic Knowledge Engineering

This book proposes a consistent methodology for building intelligent systems. It puts forward several formal models for designing and implementing rules-based systems, and presents illustrative case studies of their applications. These include software engineering, business process systems, Semantic Web, and context-aware systems on mobile devices. Rules offer an intuitive yet powerful method for representing human knowledge, and intelligent systems based on rules have many important applications. However, their practical development requires proper techniques and models - a gap that this book effectively addresses.

This book constitutes the refereed proceedings of the 12th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2000, held in Juan-les-Pins, France in October 2000. The 28 revised full papers and six revised short papers presented were carefully reviewed and selected from a high number of high-quality submissions. The book offers topical sections on knowledge modeling languages and tools, ontologies, knowledge acquisition from texts, machine learning, knowledge management and electronic commerce, problem solving methods, knowledge representation, validation, evaluation and certification, and methodologies.

"This book addresses how we can make the Web more useful, more intelligent, more knowledge intensive to fulfill our more and more demanding learning and working needs? It is based on the premise that representing knowledge visually is key for individuals and organizations to enable useful access to the knowledge era"--Provided by publisher.

Ontologies have become increasingly important as the use of knowledge graphs, machine learning, natural language processing (NLP), and the amount of data generated on a daily basis has exploded. As of 2014, 90% of the data in the digital universe was generated in the two years prior, and the volume of data was projected to grow from 3.2 zettabytes to 40 zettabytes in the next six years. The very real issues that government, research, and commercial organizations are facing in order to sift through this amount of information to support decision-making alone mandate increasing automation. Yet, the data profiling, NLP, and learning algorithms that are ground-zero for data integration, manipulation, and search provide less than satisfactory results unless they utilize terms with unambiguous semantics, such as those found in ontologies and well-formed rule sets. Ontologies can provide a rich "schema" for the knowledge graphs underlying these technologies as well as the terminological and semantic basis for dramatic improvements in results. Many ontology projects fail, however, due at least in part to a lack of discipline in the development process. This book, motivated by the Ontology 101 tutorial given for many years at what was originally the Semantic Technology Conference (SemTech) and then later from a semester-long university class, is designed to provide the foundations for ontology engineering. The book can serve as a course textbook or a primer for all those interested in ontologies.

Knowledge Engineering (KE) is a field within artificial intelligence that develops knowledgebased systems. KE is the process of imitating how a human expert in a specific domain would act and take decisions. It contains large amounts of knowledge, like metadata and information about a data object that describes characteristics such as content, quality, and format, structure and processes. Such systems are computer programs that are the basis of how a decision is made or a conclusion is reached. It is having all the rules and reasoning mechanisms to provide solutions to real-world problems. This book presents an extensive collection of the recent findings and innovative research in the information system and KE domain. Highlighting the challenges and difficulties in implementing these approaches, this book is a critical reference source for academicians, professionals, engineers, technology designers, analysts, undergraduate and postgraduate students in computing science and related disciplines such as Information systems, Knowledge Engineering, Intelligent Systems, Artifi cial Intelligence, Cognitive Neuro - science, and Robotics. In addition, anyone who is interested or involved in sophisticated information systems and knowledge engineering developments will find this book a valuable source of ideas and guidance.

Proceedings of the 10th International Conference on Human Interaction and Emerging Technologies, IHIET 2023, August 22-24, 2023, Université Côte d'Azur, Nice, France.

Written by foremost experts in the field, Engineering Modeling Languages provides end-to-end coverage of the engineering of modeling languages to turn domain knowledge into tools. The book provides a definition of different kinds of modeling languages, their instrumentation with tools such as editors, interpreters and generators, the integration of multiple modeling languages to achieve a system view, and the validation of both models and tools. Industrial case studies, across a range of application domains, are included to attest to the benefits offered by the different techniques. The book also includes a variety of simple worked examples that introduce the techniques to the novice user. The book is structured in two main parts. The first part is organized around a flow that introduces readers to Model Driven Engineering (MDE) concepts and technologies in a pragmatic manner. It starts with definitions of modeling and MDE, and then moves into a deeper discussion of how to express the knowledge of particular domains using modeling languages to ease the development of systems in the domains. The second part of the book presents examples of applications of the model-driven approach to different types of software systems. In addition to illustrating the unification power of models in different software domains, this part demonstrates applicability from different starting points (language, business knowledge, standard, etc.) and focuses on different software engineering activities such as Requirement Engineering, Analysis, Design, Implementation, and V&V. Each chapter concludes with a small set of exercises to help the reader reflect on what was learned or to dig further into the examples. Many examples of models and code snippets are presented throughout the book, and a supplemental website features all of the models and programs (and their associated tooling) discussed in the book.