This book constitutes the thoroughly refereed proceedings of the 8th Joint International Semantic Technology Conference, JIST 2018, held in Awaji, Japan, in November 2018. The 23 full papers and 6 short papers presented were carefully reviewed and selected from 75 submissions. They present applications of semantic technologies, theoretical results, new algorithms and tools to facilitate the adoption of semantic technologies and are organized in topical sections on knowledge graphs; data management; question answering and NLP; ontology and reasoning; government open data; and semantic web for life sciences.
This volume contains lecture notes of the 14th Reasoning Web Summer School (RW 2018), held in Esch-sur-Alzette, Luxembourg, in September 2018. The research areas of Semantic Web, Linked Data, and Knowledge Graphs have recently received a lot of attention in academia and industry. Since its inception in 2001, the Semantic Web has aimed at enriching the existing Web with meta-data and processing methods, so as to provide Web-based systems with intelligent capabilities such as context awareness and decision support. The Semantic Web vision has been driving many community efforts which have invested a lot of resources in developing vocabularies and ontologies for annotating their resources semantically. Besides ontologies, rules have long been a central part of the Semantic Web framework and are available as one of its fundamental representation tools, with logic serving as a unifying foundation. Linked Data is a related research area which studies how one can make RDF data available on the Web and interconnect it with other data with the aim of increasing its value for everybody. Knowledge Graphs have been shown useful not only for Web search (as demonstrated by Google, Bing, etc.) but also in many application domains.
Gain hands-on experience with SPARQL, the RDF query language that?s bringing new possibilities to semantic web, linked data, and big data projects. This updated and expanded edition shows you how to use SPARQL 1.1 with a variety of tools to retrieve, manipulate, and federate data from the public web as well as from private sources. Author Bob DuCharme has you writing simple queries right away before providing background on how SPARQL fits into RDF technologies. Using short examples that you can run yourself with open source software, you?ll learn how to update, add to, and delete data in RDF datasets. * Get the big picture on RDF, linked data, and the semantic web * Use SPARQL to find bad data and create new data from existing data * Use datatype metadata and functions in your queries * Learn techniques and tools to help your queries run more efficiently * Use RDF Schemas and OWL ontologies to extend the power of your queries * Discover the roles that SPARQL can play in your 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.
This book constitutes the refereed proceedings of the 3 rd International Workshop, SAVE-SD 2017, held in Perth, Australia, in April 2017, and the 4 th International Workshop, SAVE-SD 2018, held in Lyon, France, in April 2018. The 6 full, 2 position and 4 short papers were selected from 16 submissions. The papers describe multiple ways in which scholarly dissemination can be approved: Creating structured data, providing methods for semantic computational analysis and designing systems for navigating. This allows a variety of stakeholders to understand research dynamics, predict trends and evaluate the quality of research.
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 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 volume contains a record of some of the lectures and seminars delivered at the Third International School on Engineering Trustworthy Software Systems (SETSS 2017), held in April 2017 at Southwest University in Chongqing, China. The six contributions included in this volume provide an overview of leading-edge research in methods and tools for use in computer system engineering. They have been distilled from six original courses delivered at the school on topics such as: rely/guarantee thinking; Hoare-style specification and verification of object-oriented programs with JML; logic, specification, verification, and interactive proof; software model checking with Automizer; writing programs and proofs; engineering self-adaptive software-intensive systems; and with an additional contribution on the challenges for formal semantic description. The material is useful for postgraduate students, researchers, academics, and industrial engineers, who are interested in the theory and practice of methods and tools for the design and programming of trustworthy software systems.
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.
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.