The two-volume set LNCS 11136 and 11137 constitutes the refereed proceedings of the 17th International Semantic Web Conference, ISWC 2018, held in Monterey, USA, in October 2018. The ISWC conference is the premier international forum for the Semantic Web / Linked Data Community. The total of 62 full papers included in this volume was selected from 250 submissions. The conference is organized in three tracks: for the Research Track 39 full papers were selected from 164 submissions. The Resource Track contains 17 full papers, selected from 55 submissions; and the In-Use track features 6 full papers which were selected from 31 submissions to this track. Paper ´The SPAR Ontologies´ is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Refinement is one of the cornerstones of a formal approach to software engineering. Refinement is all about turning an abstract description (of a soft or hardware system) into something closer to implementation. It provides that essential bridge between higher level requirements and an implementation of those requirements. This book provides a comprehensive introduction to refinement for the researcher or graduate student. It introduces refinement in different semantic models, and shows how refinement is defined and used within some of the major formal methods and languages in use today. It (1) introduces the reader to different ways of looking at refinement, relating refinement to observations(2) shows how these are realised in different semantic models (3) shows how different formal methods use different models of refinement, and (4) how these models of refinement are related.
This book constitutes revised selected papers from the 12th International Workshop on Rewriting Logic and Its Applications, WRLA 2018, held in Thessaloniki, Greece, in June 2018. The 12 full papers presented in this volume were carefully reviewed and selected from 21 submissions. They deal with rewriting, a natural model of computation and an expressive semantic framework for concurrency, parallelism, communication, and interaction, and its applications.
Das Handbuch der Künstlichen Intelligenz vereint einführende und weiterführende Beiträge u.a. zu folgenden Themen: - Kognition - Neuronale Netze - Wissensrepräsentation - Unsicheres und vages Wissen - Maschinelles Lernen und Data Mining - Sprachverarbeitung - Semantic Web - Multiagentensysteme - Bildverstehen - Robotik - Software-Agenten - Universelle Spielprogramme Die 17 Kapitel von über 30 renommierten Autoren lassen sich unabhängig von einander lesen und machen das Werk zu einem aktuellen Handbuch und flexibel in der Lehre einsetzbaren Referenzwerk.
Principles of Data Integration is the first comprehensive textbook of data integration, covering theoretical principles and implementation issues as well as current challenges raised by the semantic web and cloud computing. The book offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. Readers will also learn how to build their own algorithms and implement their own data integration application. Written by three of the most respected experts in the field, this book provides an extensive introduction to the theory and concepts underlying today´s data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. This text is an ideal resource for database practitioners in industry, including data warehouse engineers, database system designers, data architects/enterprise architects, database researchers, statisticians, and data analysts; students in data analytics and knowledge discovery; and other data professionals working at the R&D and implementation levels. Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand Enables you to build your own algorithms and implement your own data integration applications
This book constitutes the refereed proceedings of the 14th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2018, held in Poznan, Poland, in September 2018, during the IFIP World Computer Congress. It consists of 38 carefully reviewed papers selected from 102 submissions. The papers are organized in topical sections, namely big data and cloud computing; architectures, structures and algorithms for efficient data processing; artificial intelligence, data mining and knowledge discovery; text mining, natural language processing, ontologies and semantic web; image analysis and multimedia mining.
This major work on knowledge representation is based on the writings of Charles S. Peirce, a logician, scientist, and philosopher of the first rank at the beginning of the 20th century. This book follows Peirce´s practical guidelines and universal categories in a structured approach to knowledge representation that captures differences in events, entities, relations, attributes, types, and concepts. Besides the ability to capture meaning and context, the Peircean approach is also well-suited to machine learning and knowledge-based artificial intelligence. Peirce is a founder of pragmatism, the uniquely American philosophy. Knowledge representation is shorthand for how to represent human symbolic information and knowledge to computers to solve complex questions. KR applications range from semantic technologies and knowledge management and machine learning to information integration, data interoperability, and natural language understanding. Knowledge representation is an essential foundation for knowledge-based AI. This book is structured into five parts. The first and last parts are bookends that first set the context and background and conclude with practical applications. The three main parts that are the meat of the approach first address the terminologies and grammar of knowledge representation, then building blocks for KR systems, and then design, build, test, and best practices in putting a system together. Throughout, the book refers to and leverages the open source KBpedia knowledge graph and its public knowledge bases, including Wikipedia and Wikidata. KBpedia is a ready baseline for users to bridge from and expand for their own domain needs and applications. It is built from the ground up to reflect Peircean principles. This book is one of timeless, practical guidelines for how to think about KR and to design knowledge management (KM) systems. The book is grounded bedrock for enterprise information and knowledge managers who are contemplating a new knowledge initiative. This book is an essential addition to theory and practice for KR and semantic technology and AI researchers and practitioners, who will benefit from Peirce´s profound understanding of meaning and context.
This book describes novel software architectures for the integration of deep and shallow natural language processing (NLP) components in language technology. The generic markup language XML and the XML transformation language XSLT are used for flexible combination of linguistic markup produced by multiple NLP components. Shallow NLP components such as tokenizers, part-of-speech taggers, named entity recognizers and shallow parsers are combined with a deep parser, operating grammars written in the spirit of the Head-Driven Phrase Structure Grammar (HPSG) theory. The integration paradigm enables synergy leading to more robust deep parsing with increased coverage. It also constitutes a division of labor: the deep grammar models general, correct language use, while shallow systems are responsible for domain-specific extensions. Applications are presented in question answering, information extraction, natural language understanding, ontologies and the Semantic Web. The book addresses to software engineers, computational linguists and language technology engineers.
Program analysis concerns static techniques for computing reliable approximate information about the dynamic behaviour of programs. Applications include compilers (for code improvement), software validation (for detecting errors in algorithms or breaches of security) and transformations between data representation (for solving problems such as the Y2K problem). This book is unique in giving an overview of the four major approaches to program analysis: data flow analysis, constraint based analysis, abstract interpretation, and type and effect systems. The presentation demonstrates the extensive similarities between the approaches; this will aid the reader in choosing the right approach and in enhancing it with insights from the other approaches. The book covers basic semantic properties as well as more advanced algorithmic techniques. The book is aimed at M.Sc. and Ph.D. students but will be valuable also for experienced researchers and professionals.
´´Probabilistic Reasoning in Intelligent Systems´´ is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. ´´Probabilistic Reasoning in Intelligent Systems´´ will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI,operations research, or applied probability.