Taxonomy Matching Using Background Knowledge: Linked Data, Semantic Web and Heterogeneous Repositories, Angermann Heiko, Ramzan Naeem
Автор: Angermann Название: Taxonomy Matching Using Background Knowledge ISBN: 3319722085 ISBN-13(EAN): 9783319722085 Издательство: Springer Рейтинг: Цена: 32600.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This important text/reference presents a comprehensive review of techniques for taxonomy matching, discussing matching algorithms, analyzing matching systems, and comparing matching evaluation approaches.
Автор: Chen Название: Knowledge Graph and Semantic Computing: Semantic, Knowledge, and Linked Big Data ISBN: 9811031673 ISBN-13(EAN): 9789811031670 Издательство: Springer Рейтинг: Цена: 55900.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the first China Conference on Knowledge Graph and Semantic Computing, CCKS, held in Beijing, China, in September 2016.The 19 revised full papers presented together with 6 shared tasks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on knowledge representation and learning; knowledge graph construction and information extraction; linked data and knowledge-based systems; shared tasks.
Автор: Eero Hyvonen Название: Publishing and Using Cultural Heritage Linked Data on the Semantic Web ISBN: 1608459977 ISBN-13(EAN): 9781608459971 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 41580.00 T Наличие на складе: Невозможна поставка. Описание: Offers an overview on why, when, and how Linked (Open) Data and Semantic Web technologies can be employed in practice in publishing Cultural Heritage (CH) collections and other content on the Web. The text presents a general semantic portal model and publishing framework as a solution approach to distributed semantic content creation, based on an ontology infrastructure.
Автор: Ord??ez De Pablos, Lytras, Tenny Название: Cases On Open-Linked Data And Semantic Web Applications ISBN: 1466628278 ISBN-13(EAN): 9781466628274 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 170010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: With the purpose of building upon standard web technologies, open linked data serves as a useful way to connect previously unrelated data and to publish structured data on the web. The application of these elements leads to the creation of data commons called semantic web. <br><br><em>Cases on Open-Linked Data and Semantic Web Applications</em> brings together new theories, research findings and case studies which cover the recent developments and approaches towards applied open linked data and semantic web in the context of information systems. By enhancing the understanding of open linked data in business, science and information technologies, this reference source aims to be useful for academics, researchers, and practitioners. With the purpose of building upon standard web technologies, open linked data serves as a useful way to connect previously unrelated data and to publish structured data on the web. The application of these elements leads to the creation of data commons called semantic web.
Автор: Pan Jeff Z., Vetere Guido, Gomez-Perez Jose Manuel Название: Exploiting Linked Data and Knowledge Graphs in Large Organisations ISBN: 3319833391 ISBN-13(EAN): 9783319833392 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Part I Knowledge Graph Foundations & Architecture.- Part II Constructing, Understanding and Consuming Knowledge Graphs.- Part III Industrial Applications and Successful Stories.
Название: Linked data and knowledge graph ISBN: 3642540244 ISBN-13(EAN): 9783642540240 Издательство: Springer Рейтинг: Цена: 52170.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the Seventh Chinese Semantic Web Symposium and the Second Chinese Web Science Conference, CSWS 2013, held in Shanghai, China in August 2013. The 14 revised full papers presented together with 6 short papers and 3 poster papers were carefully reviewed and selected from 48 submissions.
Автор: S?ren Auer; Volha Bryl; Sebastian Tramp Название: Linked Open Data -- Creating Knowledge Out of Interlinked Data ISBN: 3319098454 ISBN-13(EAN): 9783319098456 Издательство: Springer Рейтинг: Цена: 44720.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Linked Open Data (LOD) is a pragmatic approach for realizing the Semantic Web vision of making the Web a global, distributed, semantics-based information system. Commencing in September 2010, this 4-year project comprised leading Linked Open Data research groups, companies, and service providers from across 11 European countries and South Korea.
Автор: Jeff Z. Pan; Guido Vetere; Jose Manuel Gomez-Perez Название: Exploiting Linked Data and Knowledge Graphs for Large Organisations ISBN: 3319456520 ISBN-13(EAN): 9783319456522 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book addresses the topic of exploiting enterprise-linked data with a particular focus on knowledge construction and accessibility within enterprises. It identifies the gaps between the requirements of enterprise knowledge consumption and "standard" data consuming technologies by analysing real-world use cases, and proposes the enterprise knowledge graph to fill such gaps. It provides concrete guidelines for effectively deploying linked-data graphs within and across business organizations. It is divided into three parts, focusing on the key technologies for constructing, understanding and employing knowledge graphs. Part 1 introduces basic background information and technologies, and presents a simple architecture to elucidate the main phases and tasks required during the lifecycle of knowledge graphs. Part 2 focuses on technical aspects; it starts with state-of-the art knowledge-graph construction approaches, and then discusses exploration and exploitation techniques as well as advanced question-answering topics concerning knowledge graphs. Lastly, Part 3 demonstrates examples of successful knowledge graph applications in the media industry, healthcare and cultural heritage, and offers conclusions and future visions.
Автор: Nikolaos Konstantinou; Dimitrios-Emmanuel Spanos Название: Materializing the Web of Linked Data ISBN: 331935745X ISBN-13(EAN): 9783319357454 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book explains the Linked Data domain by adopting a bottom-up approach: it introduces the fundamental Semantic Web technologies and building blocks, which are then combined into methodologies and end-to-end examples for publishing datasets as Linked Data, and use cases that harness scholarly information and sensor data.
Автор: Carol Jean Godby, Shenghui Wang, Jeffrey K. Mixter Название: Library Linked Data in the Cloud: OCLC`s Experiments with New Models of Resource Description ISBN: 1627052194 ISBN-13(EAN): 9781627052191 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 51750.00 T Наличие на складе: Невозможна поставка. Описание: This book focuses on the conceptual and technical challenges involved in publishing linked data derived from traditional library metadata. This transformation is a high priority because most searches for information start not in the library, nor even in a Web-accessible library catalogue, but elsewhere on the Internet.
Автор: Sakr Название: Linked Data ISBN: 3319735144 ISBN-13(EAN): 9783319735146 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
This book describes efficient and effective techniques for harnessing the power of Linked Data by tackling the various aspects of managing its growing volume: storing, querying, reasoning, provenance management and benchmarking.
To this end, Chapter 1 introduces the main concepts of the Semantic Web and Linked Data and provides a roadmap for the book. Next, Chapter 2 briefly presents the basic concepts underpinning Linked Data technologies that are discussed in the book. Chapter 3 then offers an overview of various techniques and systems for centrally querying RDF datasets, and Chapter 4 outlines various techniques and systems for efficiently querying large RDF datasets in distributed environments. Subsequently, Chapter 5 explores how streaming requirements are addressed in current, state-of-the-art RDF stream data processing. Chapter 6 covers performance and scaling issues of distributed RDF reasoning systems, while Chapter 7 details benchmarks for RDF query engines and instance matching systems. Chapter 8 addresses the provenance management for Linked Data and presents the different provenance models developed. Lastly, Chapter 9 offers a brief summary, highlighting and providing insights into some of the open challenges and research directions.
Providing an updated overview of methods, technologies and systems related to Linked Data this book is mainly intended for students and researchers who are interested in the Linked Data domain. It enables students to gain an understanding of the foundations and underpinning technologies and standards for Linked Data, while researchers benefit from the in-depth coverage of the emerging and ongoing advances in Linked Data storing, querying, reasoning, and provenance management systems. Further, it serves as a starting point to tackle the next research challenges in the domain of Linked Data management.
Автор: Nikolaos Konstantinou; Dimitrios-Emmanuel Spanos Название: Materializing the Web of Linked Data ISBN: 3319160737 ISBN-13(EAN): 9783319160733 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book explains the Linked Data domain by adopting a bottom-up approach: it introduces the fundamental Semantic Web technologies and building blocks, which are then combined into methodologies and end-to-end examples for publishing datasets as Linked Data, and use cases that harness scholarly information and sensor data.
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