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Abstract

Ontologies are viewed as the silver bullet for many applications, but in open or evolving systems, different parties can adopt different ontologies. This increases heterogeneity problems rather than reducing heterogeneity. This book proposes ontology matching as a solution to the problem of semantic heterogeneity, offering researchers and practitioners a uniform framework of reference to currently available work. The techniques presented apply to database schema matching, catalog integration, XML schema matching and more. Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaiko's book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, artificial intelligence. With Ontology Matching, researchers and practitioners will find a reference book which presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can equally be applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a detailed account of matching techniques and matching systems in a systematic way from theoretical, practical and application perspectives.

Date

20. 6.2012 19:08:22

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Abstract

Künstliche Intelligenz (KI) steht für Maschinen, die können, was der Mensch kann: hören und sehen, sprechen, lernen, Probleme lösen. In manchem sind sie inzwischen nicht nur schneller, sondern auch besser als der Mensch. Wie funktionieren diese klugen Maschinen? Bedrohen sie uns, machen sie uns gar überflüssig? Die Journalistin und KI-Expertin Manuela Lenzen erklärt anschaulich, was Künstliche Intelligenz kann und was uns erwartet. Künstliche Intelligenz ist das neue Zauberwort des digitalen Kapitalismus. Intelligente Computersysteme stellen medizinische Diagnosen und geben Rechtsberatung. Sie managen den Aktienhandel und steuern bald unsere Autos. Sie malen, dichten, dolmetschen und komponieren. Immer klügere Roboter stehen an den Fließbändern, begrüßen uns im Hotel, führen uns durchs Museum oder braten Burger und schnipseln den Salat dazu. Doch neben die Utopie einer schönen neuen intelligenten Technikwelt sind längst Schreckbilder getreten: von künstlichen Intelligenzen, die uns auf Schritt und Tritt überwachen, die unsere Arbeitsplätze übernehmen und sich unserer Kontrolle entziehen. Manuela Lenzen zeigt, welche Hoffnungen und Befürchtungen realistisch sind und welche in die Science Fiction gehören. Sie beschreibt, wie ein gutes Leben mit der Künstlichen Intelligenz aussehen könnte - und dass wir von klugen Maschinen eine Menge über uns selbst lernen können.

Date

18. 6.2018 19:22:02

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Abstract

Metadata research has emerged as a discipline cross-cutting many domains, focused on the provision of distributed descriptions (often called annotations) to Web resources or applications. Such associated descriptions are supposed to serve as a foundation for advanced services in many application areas, including search and location, personalization, federation of repositories and automated delivery of information. Indeed, the Semantic Web is in itself a concrete technological framework for ontology-based metadata. For example, Web-based social networking requires metadata describing people and their interrelations, and large databases with biological information use complex and detailed metadata schemas for more precise and informed search strategies. There is a wide diversity in the languages and idioms used for providing meta-descriptions, from simple structured text in metadata schemas to formal annotations using ontologies, and the technologies for storing, sharing and exploiting meta-descriptions are also diverse and evolve rapidly. In addition, there is a proliferation of schemas and standards related to metadata, resulting in a complex and moving technological landscape - hence, the need for specialized knowledge and skills in this area. The Handbook of Metadata, Semantics and Ontologies is intended as an authoritative reference for students, practitioners and researchers, serving as a roadmap for the variety of metadata schemas and ontologies available in a number of key domain areas, including culture, biology, education, healthcare, engineering and library science.

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Abstract

Practical Ontologies for Information Professionals provides an accessible introduction and exploration of ontologies and demonstrates their value to information professionals. More data and information is being created than ever before. Ontologies, formal representations of knowledge with rich semantic relationships, have become increasingly important in the context of today's information overload and data deluge. The publishing and sharing of explicit explanations for a wide variety of conceptualizations, in a machine readable format, has the power to both improve information retrieval and discover new knowledge. Information professionals are key contributors to the development of new, and increasingly useful, ontologies. Practical Ontologies for Information Professionals provides an accessible introduction to the following: defining the concept of ontologies and why they are increasingly important to information professionals ontologies and the semantic web existing ontologies, such as RDF, RDFS, SKOS, and OWL2 adopting and building ontologies, showing how to avoid repetition of work and how to build a simple ontology interrogating ontologies for reuse the future of ontologies and the role of the information professional in their development and use. This book will be useful reading for information professionals in libraries and other cultural heritage institutions who work with digitalization projects, cataloguing and classification and information retrieval. It will also be useful to LIS students who are new to the field.

Content

C H A P T E R 1 What is an ontology?; Introduction; The data deluge and information overload; Defining terms; Knowledge organization systems and ontologies; Ontologies, metadata and linked data; What can an ontology do?; Ontologies and information professionals; Alternatives to ontologies; The aims of this book; The structure of this book; C H A P T E R 2 Ontologies and the semantic web; Introduction; The semantic web and linked data; Resource Description Framework (RDF); Classes, subclasses and properties; The semantic web stack; Embedded RDF; Alternative semantic visionsLibraries and the semantic web; Other cultural heritage institutions and the semantic web; Other organizations and the semantic web; Conclusion; C H A P T E R 3 Existing ontologies; Introduction; Ontology documentation; Ontologies for representing ontologies; Ontologies for libraries; Upper ontologies; Cultural heritage data models; Ontologies for the web; Conclusion; C H A P T E R 4 Adopting ontologies; Introduction; Reusing ontologies: application profiles and data models; Identifying ontologies; The ideal ontology discovery tool; Selection criteria; Conclusion C H A P T E R 5 Building ontologiesIntroduction; Approaches to building an ontology; The twelve steps; Ontology development example: Bibliometric Metrics Ontology element set; Conclusion; C H A P T E R 6 Interrogating ontologies; Introduction; Interrogating ontologies for reuse; Interrogating a knowledge base; Understanding ontology use; Conclusion; C H A P T E R 7 The future of ontologies and the information professional; Introduction; The future of ontologies for knowledge discovery; The future role of library and information professionals; The practical development of ontologies

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LCSH

Cognitive science

Subject

Cognitive science

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Abstract

Künstliche Intelligenz ist unsere unausweichliche Zukunft. Wird sie uns ins Verderben stürzen oder zur Weiterentwicklung des Homo sapiens beitragen? Die Nobelpreis-Schmiede Massachusetts Institute of Technology ist der bedeutendste technologische Think Tank der USA. Dort arbeitet Professor Max Tegmark mit den weltweit führenden Entwicklern künstlicher Intelligenz zusammen, die ihm exklusive Einblicke in ihre Labors gewähren. Die Erkenntnisse, die er daraus zieht, sind atemberaubend und zutiefst verstörend zugleich. Neigt sich die Ära der Menschen dem Ende zu? Der Physikprofessor Max Tegmark zeigt anhand der neusten Forschung, was die Menschheit erwartet. Hier eine Auswahl möglicher Szenarien: - Eroberer: Künstliche Intelligenz übernimmt die Macht und entledigt sich der Menschheit mit Methoden, die wir noch nicht einmal verstehen. - Der versklavte Gott: Die Menschen bemächtigen sich einer superintelligenten künstlichen Intelligenz und nutzen sie, um Hochtechnologien herzustellen. - Umkehr: Der technologische Fortschritt wird radikal unterbunden und wir kehren zu einer prä-technologischen Gesellschaft im Stil der Amish zurück. - Selbstzerstörung: Superintelligenz wird nicht erreicht, weil sich die Menschheit vorher nuklear oder anders selbst vernichtet. - Egalitäres Utopia: Es gibt weder Superintelligenz noch Besitz, Menschen und kybernetische Organismen existieren friedlich nebeneinander. Max Tegmark bietet kluge und fundierte Zukunftsszenarien basierend auf seinen exklusiven Einblicken in die aktuelle Forschung zur künstlichen Intelligenz.