Social Tagging in a Linked Data Environment, Diane Rasmussen Pennington, Louise Spiteri
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.
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: 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: Russell Matthew A., Klassen Mikhail Название: Mining the Social Web: Data Mining Facebook, Twitter, Linkedin, Google+, Github, and More ISBN: 1491985046 ISBN-13(EAN): 9781491985045 Издательство: Wiley Рейтинг: Цена: 47510.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media.
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.
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: Diana Trandab??; Daniela G?fu Название: Linguistic Linked Open Data ISBN: 3319329413 ISBN-13(EAN): 9783319329413 Издательство: Springer Рейтинг: Цена: 46590.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Ontological modeling of social media data.- Application of social media and linked data methodologies in real-life scenarios.- User proling and assessing the suitability of content from social media.- Extracting and linking content.- Sentiment analysis in social media and linked data.-Social data mining to create structured social media resources.
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.
Автор: Pennington Diane Название: Social Tagging for Linking Data Across Environments ISBN: 1783303387 ISBN-13(EAN): 9781783303380 Издательство: Facet Рейтинг: Цена: 109120.00 T Наличие на складе: Нет в наличии. Описание: This book, representing researchers and practitioners across different information professions, will explore how social tags can link content across a variety of environments.
Автор: Seth van Hooland, Ruben Verborgh Название: Linked Data for Libraries, Archives and Museums: How to clean, link and publish your metadata ISBN: 1783303018 ISBN-13(EAN): 9781783303014 Издательство: Facet Рейтинг: Цена: 183040.00 T Наличие на складе: Невозможна поставка. Описание: This highly practical handbook teaches you how to unlock the value of your existing metadata through cleaning, reconciliation, enrichment and linking and how to streamline the process of new metadata creation.
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: Witold Pedrycz; Shyi-Ming Chen Название: Data Science and Big Data: An Environment of Computational Intelligence ISBN: 3319534734 ISBN-13(EAN): 9783319534732 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Part I. Fundamentals.- Large-Scale Clustering Algorithms.- On High Dimensional Search Space and Learning Methods.-Enhanced Over_Sampling Techniques for Imbalanced Big Data Set Classification.- Online Anomaly Detection in Big Data: The First Line of Defense Against Intruders.- Developing Modified Classifier for Big Data Paradigm: An Approach through Bio-Inspired Soft Computing.- Unified Framework for Control of Machine Learning Tasks Towards Effective and Efficient Processing of Big Data.- An Efficient Approach for Mining High Utility Itemsets over Data Streams.- Event Detection in Location-Based Social Networks.- Part II. Applications.- Using Computational Intelligence for the Safety Assessment of Oil and Gas Pipelines: A Survey.- Big Data for Effective Management of Smart Grids.- Distributed Machine Learning on Smart-Gateway Network Towards Real-Time Indoor Data Analytics.- Predicting Spatiotemporal Impacts of Weather on Power Systems using Big Data Science.- Index.
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