Knowledge Graphs, Hogan Aidan, Blomqvist Eva, Cochez Michael
Автор: Leskovec Jure Название: Mining of Massive Datasets ISBN: 1108476341 ISBN-13(EAN): 9781108476348 Издательство: Cambridge Academ Рейтинг: Цена: 71810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.
Название: Research 2.0 and the impact of digital technologies on scholarly inquiry / ISBN: 1522508309 ISBN-13(EAN): 9781522508304 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 126130.00 T Наличие на складе: Есть Описание: The academic landscape has been significantly enhanced by the advent of new technology. These tools allow researchers easier information access to better increase their knowledge base.Research 2.0 and the Impact of Digital Technologies on Scholarly Inquiry is an authoritative reference source for the latest insights on the impact of web services and social technologies for conducting academic research. Highlighting international perspectives, emerging scholarly practices, and real-world contexts, this book is ideally designed for academicians, practitioners, upper-level students, and professionals interested in the growing field of digital scholarship.
Автор: by Laura Po; Nikos Bikakis; Federico Desimoni Название: Linked Data Visualization ISBN: 1681737256 ISBN-13(EAN): 9781681737256 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 53590.00 T Наличие на складе: Нет в наличии. Описание: Linked Data (LD) is a well-established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from different scientific and commercial domains.
The development of Linked Data Visualization techniques and tools has been followed as the primary means for the analysis of this vast amount of information by data scientists, domain experts, business users, and citizens.
This book covers a wide spectrum of visualization issues, providing an overview of the recent advances in this area, focusing on techniques, tools, and use cases of visualization and visual analysis of LD. It presents the basic concepts related to data visualization and the LD technologies, the techniques employed for data visualization based on the characteristics of data techniques for Big Data visualization, use tools and use cases in the LD context, and finally a thorough assessment of the usability of these tools under different scenarios.
The purpose of this book is to offer a complete guide to the evolution of LD visualization for interested readers from any background and to empower them to get started with the visual analysis of such data. This book can serve as a course textbook or a primer for all those interested in LD and data visualization.
Автор: Charu C. Aggarwal Название: Machine Learning for Text ISBN: 3030088073 ISBN-13(EAN): 9783030088071 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories:- Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for machine learning from text such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.- Domain-sensitive mining: Chapters 8 and 9 discuss the learning methods from text when combined with different domains such as multimedia and the Web. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. - Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This textbook covers machine learning topics for text in detail. Since the coverage is extensive,multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer courses not just in text analytics but also from the broader perspective of machine learning (with text as a backdrop). This textbook targets graduate students in computer science, as well as researchers, professors, and industrial practitioners working in these related fields. This textbook is accompanied with a solution manual for classroom teaching.
Автор: Radu Tudor Ionescu; Marius Popescu Название: Knowledge Transfer between Computer Vision and Text Mining ISBN: 3319303651 ISBN-13(EAN): 9783319303659 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Нет в наличии. Описание: This ground-breaking text/reference divergesfrom the traditional view that computer vision (for image analysis) and stringprocessing (for text mining) are separate and unrelated fields of study,propounding that images and text can be treated in a similar manner for thepurposes of information retrieval, extraction and classification.
Автор: Zhengming Ding; Handong Zhao; Yun Fu Название: Learning Representation for Multi-View Data Analysis ISBN: 3030007332 ISBN-13(EAN): 9783030007331 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book equips readers to handle complex multi-view data representation, centered around several major visual applications, sharing many tips and insights through a unified learning framework. This framework is able to model most existing multi-view learning and domain adaptation, enriching readers’ understanding from their similarity, and differences based on data organization and problem settings, as well as the research goal.A comprehensive review exhaustively provides the key recent research on multi-view data analysis, i.e., multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. More practical challenges in multi-view data analysis are discussed including incomplete, unbalanced and large-scale multi-view learning. Learning Representation for Multi-View Data Analysis covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.
Автор: Ashok N. Srivastava, Mehran Sahami Название: Text Mining ISBN: 1420059408 ISBN-13(EAN): 9781420059403 Издательство: Taylor&Francis Рейтинг: Цена: 100030.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Focuses on statistical methods for text mining and analysis. This work examines methods to automatically cluster and classify text documents and applies these methods in a variety of areas, including adaptive information filtering, information distillation, and text search.
Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed.
Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants.
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.
This book describes a set of methods, architectures, and tools to extend the data pipeline at the disposal of developers when they need to publish and consume data from Knowledge Graphs (graph-structured knowledge bases that describe the entities and relations within a domain in a semantically meaningful way) using SPARQL, Web APIs, and JSON. To do so, it focuses on the paradigmatic cases of two middleware software packages, grlc and SPARQL Transformer, which automatically build and run SPARQL-based REST APIs and allow the specification of JSON schema results, respectively.
The authors highlight the underlying principles behind these technologies—query management, declarative languages, new levels of indirection, abstraction layers, and separation of concerns—, explain their practical usage, and describe their penetration in research projects and industry.
The book, therefore, serves a double purpose: to provide a sound and technical description of tools and methods at the disposal of publishers and developers to quickly deploy and consume Web Data APIs on top of Knowledge Graphs; and to propose an extensible and heterogeneous Knowledge Graph access infrastructure that accommodates a growing ecosystem of querying paradigms.
This book describes a set of methods, architectures, and tools to extend the data pipeline at the disposal of developers when they need to publish and consume data from Knowledge Graphs (graph-structured knowledge bases that describe the entities and relations within a domain in a semantically meaningful way) using SPARQL, Web APIs, and JSON. To do so, it focuses on the paradigmatic cases of two middleware software packages, grlc and SPARQL Transformer, which automatically build and run SPARQL-based REST APIs and allow the specification of JSON schema results, respectively.
The authors highlight the underlying principles behind these technologies—query management, declarative languages, new levels of indirection, abstraction layers, and separation of concerns—, explain their practical usage, and describe their penetration in research projects and industry.
The book, therefore, serves a double purpose: to provide a sound and technical description of tools and methods at the disposal of publishers and developers to quickly deploy and consume Web Data APIs on top of Knowledge Graphs; and to propose an extensible and heterogeneous Knowledge Graph access infrastructure that accommodates a growing ecosystem of querying paradigms.
Автор: Hogan Aidan, Blomqvist Eva, Cochez Michael Название: Knowledge Graphs ISBN: 1636392350 ISBN-13(EAN): 9781636392356 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 89630.00 T Наличие на складе: Нет в наличии. Описание:
This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale.
The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve.
This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.
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