%{search_type} search results

• Intro; Title Page; Introduction; Motivation; Challenges; Hypotheses and Research Questions; Contributions; Outline; Foundations; Semantic Web; Representation Learning; Data Fusion; Introduction; Motivating Example; Related Work; Pipeline; Experiments; Summary; Multi-modal Fusion and Transfer; Introduction; Motivating Example; Related Work; Multi-modal Fusion; Experiments on Multi-modal Fusion; Multi-modal Transfer; Experiments on Multi-modal Transfer; Summary; Conclusion; Summary; Future Work; Bibliography; Appendix; Full Michael Jordan Example; Evaluation Tables; Evaluation Heatmaps

Many web pages include structured data in the form of semantic markup, which can be transferred to the Resource Description Framework (RDF) or provide an interface to retrieve RDF data directly. This RDF data enables machines to automatically process and use the data. When applications need data from more than one source the data has to be integrated, and the automation of this can be challenging. Usually, vocabularies are used to concisely describe the data, but because of the decentralized nature of the web, multiple data sources can provide similar information with different vocabularies, making integration more difficult. This book, Multi-modal Data Fusion based on Embeddings, describes how similar statements about entities can be identified across sources, independent of the vocabulary and data modeling choices. Previous approaches have relied on clean and extensively modeled ontologies for the alignment of statements, but the often noisy data in a web context does not necessarily adhere to these prerequisites. In this book, the use of RDF label information of entities is proposed to tackle this problem. In combination with embeddings, the use of label information allows for a better integration of noisy data, something that has been empirically confirmed by experiment. The book presents two main scientific contributions: the vocabulary and modeling agnostic fusion approach on the purely textual label information, and the combination of three different modalities into one multi-modal embedding space for a more human-like notion of similarity. The book will be of interest to all those faced with the problem of processing data from multiple web-based sources.

• Machine generated contents note: ch. 1 Introduction
• 1.1. Linked Data
• 1.2. Entity Name System
• 1.3. Research Question and Thesis
• 1.4. Dissertation
• 1.5. Contributions
• ch. 2 Background
• 2.1. Structured Data Models
• 2.1.1. Resource Description Framework (RDF)
• 2.1.2. Relational Database (RDB) Model
• 2.1.3. Serializing RDF Data
• 2.2. Instance Matching
• 2.2.1. Blocking Step
• 2.2.2. Similarity Step
• 2.2.3. Evaluating Instance Matching
• 2.3. Heterogeneity
• 2.3.1. Type Heterogeneity
• 2.3.2. Property Heterogeneity
• 2.3.3. Extending the Two-Step Workflow
• 2.4. Scalability
• 2.4.1. Motivation
• 2.4.2. Implementation
• ch. 3 Related Work
• 3.1. Existing Domain-Independent Systems
• 3.1.1. Systems Addressing Automation
• 3.1.2. Systems Addressing Heterogeneity
• 3.1.3. Systems Addressing Scalability
• 3.1.4. Other Systems
• 3.2. Discussion
• 3.2.1. Automation vs. Scalability
• 3.2.2. Issues of Structural Heterogeneity
• 3.3.3. Issues of Unsupervised Blocking
• ch. 4 Type Alignment
• 4.1. Motivating Example and Preliminaries: A Review
• 4.2. Applications of Type Alignment
• 4.3. Approach
• 4.3.1. Possible Strategy Implementations
• 4.4. Evaluations
• 4.4.1. Test Cases
• 4.4.2. Metrics and Methodology
• 4.4.3. Results and Discussion
• ch. 5 Training Set Generation
• 5.1. Intuition
• 5.2. Approach
• 5.3. Evaluations
• 5.3.1. Test Suite
• 5.3.2. Metrics
• 5.3.3. Setup
• 5.3.4. Results and Discussion
• ch. 6 Property Alignment
• 6.1. Approach
• 6.2. Evaluations
• 6.2.1. Setup
• 6.2.2. Results and Discussion
• ch. 7 Blocking and Classification
• 7.1. Approach
• 7.1.1. Feature Generator
• 7.1.2. Learning Procedures
• 7.2. Evaluations
• 7.2.1. Blocking
• 7.2.2. Similarity (non-iterative run)
• 7.2.3. Similarity (iterative run)
• ch. 8 Scalability
• 8.1. Summary of Algorithms
• 8.2. Motivation and Use-Cases
• 8.3. MapReduce Implementations
• 8.3.1. Type Alignment
• 8.3.2. Training Set Generator
• 8.3.3. Property Alignment and Learning Procedures
• 8.3.4. Blocking and Similarity
• ch. 9 Conclusion
• 9.1. Summary
• 9.2. Future Work
• 9.2.1. Linked Data Quality
• 9.2.2. Schema-Free Approaches
• 9.2.3. Transfer Learning.

• RDF Data and Management.- Fuzzy Sets and Fuzzy Database Modeling.- Fuzzy RDF Modeling.- Persistence of Fuzzy RDF and Fuzzy RDF Schema.- Fuzzy RDF Queries.
• (source: Nielsen Book Data)

This book systemically presents the latest research findings in fuzzy RDF data modeling and management. Fuzziness widely exist in many data and knowledge intensive applications. With the increasing amount of metadata available, efficient and scalable management of massive semantic data with uncertainty is of crucial importance. This book goes to great depth concerning the fast-growing topic of technologies and approaches of modeling and managing fuzzy metadata with Resource Description Framework (RDF) format. Its major topics include representation of fuzzy RDF data, fuzzy RDF graph matching, query of fuzzy RDF data, and persistence of fuzzy RDF data in diverse databases. The objective of the book is to provide the state-of-the-art information to researchers, practitioners, and postgraduates students who work on the area of big data intelligence and at the same time serve as the uncertain data and knowledge engineering professional as a valuable real-world reference.
(source: Nielsen Book Data)

• Preface Library Standards and the Semantic Web Modeling Library Authority Files Modeling and Discovering Creative Works Entity Identification Through Text Mining The Library Linked Data Cloud Bibliography Authors' Biographies.
• (source: Nielsen Book Data)

This book describes OCLC's contributions to the transformation of the Internet from a web of documents to a Web of Data. The new Web is a growing `cloud' of interconnected resources that identify the things people want to know about when they approach the Internet with an information need. The linked data architecture has achieved critical mass just as it has become clear that library standards for resource description are nearing obsolescence. Working for the world's largest library cooperative, OCLC researchers have been active participants in the development of next-generation standards for library resource description. By engaging with an international community of library and Web standards experts, they have published some of the most widely used RDF datasets representing library collections and librarianship. 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 catalog, but elsewhere on the Internet. Modeling data in a form that the broader Web understands will project the value of libraries into the Digital Information Age. The exposition is aimed at librarians, archivists, computer scientists, and other professionals interested in modeling bibliographic descriptions as linked data. It aims to achieve a balanced treatment of theory, technical detail, and practical application.
(source: Nielsen Book Data)

• Title Page
• Contents
• Chapter 1. Introduction
• Chapter 2. State of the Art
• Chapter 3. Objectives and Contributions
• Chapter 4. Problem Formalisation
• Chapter 5. Accessing Distributed RDF Data Stores
• Chapter 6. Evaluation
• Chapter 7. Conclusions and Future Work
• Chapter 8. Updated Discussion
• Chapter A. Appendix A
• References

During the last years, the amount of RDF data has increased exponentially over the Web, exposed via SPARQL endpoints. These SPARQL endpoints allow users to direct SPARQL queries to the RDF data. Federated SPARQL query processing allows to query several of these RDF databases as if they were a single one, integrating the results from all of them. This is a key concept in the Web of Data and it is also a hot topic in the community. Besides of that, the W3C SPARQL-WG has standardized it in the new Recommendation SPARQL 1.1. This book provides a formalisation of the W3C proposed recommendation. Thi.

• Introduction: RDF and Linked Open Data RDF Ecosystem Database technologies Native RDF storage approaches Non-native RDF storage approaches Conclusion.
• (source: Nielsen Book Data)

RDF Database Systems is a cutting-edge guide that distills everything you need to know to effectively use or design an RDF database. This book starts with the basics of linked open data and covers the most recent research, practice, and technologies to help you leverage semantic technology. With an approach that combines technical detail with theoretical background, this book shows how to design and develop semantic web applications, data models, indexing and query processing solutions.
(source: Nielsen Book Data)

• Introduction Preliminaries Cloud-Based RDF Storage Cloud-Based SPARQL Query Processing SPARQL Query Optimization for the Cloud RDFS Reasoning in the Cloud Concluding Remarks Bibliography Authors' Biographies.
• (source: Nielsen Book Data)

Resource Description Framework (or RDF, in short) is set to deliver many of the original semi-structured data promises: flexible structure, optional schema, and rich, flexible Universal Resource Identifiers as a basis for information sharing. Moreover, RDF is uniquely positioned to benefit from the efforts of scientific communities studying databases, knowledge representation, and Web technologies. As a consequence, the RDF data model is used in a variety of applications today for integrating knowledge and information: in open Web or government data via the Linked Open Data initiative, in scientific domains such as bioinformatics, and more recently in search engines and personal assistants of enterprises in the form of knowledge graphs. Managing such large volumes of RDF data is challenging due to the sheer size, heterogeneity, and complexity brought by RDF reasoning. To tackle the size challenge, distributed architectures are required. Cloud computing is an emerging paradigm massively adopted in many applications requiring distributed architectures for the scalability, fault tolerance, and elasticity features it provides. At the same time, interest in massively parallel processing has been renewed by the MapReduce model and many follow-up works, which aim at simplifying the deployment of massively parallel data management tasks in a cloud environment. In this book, we study the state-of-the-art RDF data management in cloud environments and parallel/distributed architectures that were not necessarily intended for the cloud, but can easily be deployed therein. After providing a comprehensive background on RDF and cloud technologies, we explore four aspects that are vital in an RDF data management system: data storage, query processing, query optimization, and reasoning. We conclude the book with a discussion on open problems and future directions.
(source: Nielsen Book Data)

• Introducing Linked Data
• RDF: the data model for Linked Data
• Consuming Linked Data
• Creating Linked Data with FOAF
• SPARQL: querying the Linked Data Web
• Enhancing results from search engines
• RDF database fundamentals
• Datasets
• Callimachus: a Linked Data management system
• Publishing Linked Data : recap
• The evolving Web.

DESCRIPTION Linked Data is a standards-driven model for representing structured data on the Web that gives developers, publishers, and information architects a consistent, predictable way to publish, merge and consume data. It's been adopted by many well-known institutions, including Google, Facebook, IBM, Oracle, and government agencies, as well projects such as Drupal and WordPress. Linked Data presents the Linked Data model in plain, jargon-free language and offers practical techniques using everyday tools like JavaScript and Python. It works through examples of increasing complexity while explaining foundational concepts such as HTTP URIs, the Resource Description Framework (RDF), and the SPARQL query language. Readers will learn to use various Linked Data document formats to create powerful Web applications and mashups, and to effectively use emerging Web standards to access, find, and query structured data on the Web. RETAIL SELLING POINTS Written by Web developers for web developers A step-by-step, hands-on guide to using Linked Data Shows how to utilize the power of tomorrow's Web today AUDIENCE Written for Web developers by Web developers, this book requires no previous exposure to Linked Data technologies. ABOUT THE TECHNOLOGY The launch of Schema.org in June 2011 by Google, Microsoft and Yahoo! and the publication of Linked Data by retailers such as Best Buy, Sears and Volkswagen brought Linked Data into the mainstream. Linked Data is a standards-driven model for representing structured data on the Web that gives developers, publishers, and information architects a consistent, predictable way to publish, merge and consume data.
(source: Nielsen Book Data)

• An introduction to ORE
• Exploring object reuse and exchange
• Serializing and exposing resource maps
• Implementations of ORE
• Resources.

Library Technology Reports, May/June 2010 (46:4) If your job involves working with digital content, your need to make sense of interoperable digital information by managing resources with care and quality metadata and by connecting users to resources-and resources to resources-is greater than ever. In this issue of Library Technology Reports, Michael Witt helps you do just that. If you are an Electronic Resources Librarian, Digital Archivist or work with Digital Catalogs in any capacity, this report is a must-read for you. The Open Archives Initiative Object Reuse and Exchange specification defines a set of new standards for the description and exchange of aggregations of web resources. This presents an exciting opportunity to revisit how digital libraries are managing. ORE and its concept of aggregation-that a set of digital objects of different types and from different locations on the web can be described and exposed together as a single, compound entity-may present the next major disruptive technology for librarians who develop and manage collections of digital information. This technology could change your job. Michael Witt is the interdisciplinary research librarian and assistant professor of library science at Purdue University in West Lafayette, Indiana, and an expert on the technology behind digital content management. Through real-world examples, extensive diagrams and careful explanation, he details the potential of this exciting new technology, and how it can make the management and searching of your digital content more effective and efficient. Topics Covered in this Issue Include: An Introduction to ORE Exploring Object Reuse and Exchange Serializing and Exposing Resource Maps Implementations of ORE Resources.
(source: Nielsen Book Data)

• Linked open data and the cultural heritage landscape / Hilary K. Thorsen and M. Christina Pattuelli
• Making MARC agnostic : transforming the English short title catalogue for the linked data universe / Carl Stahmer
• Authority control for the Web : integrating library practice with linked data / Alison Jai O'Dell
• Linked data implications for authority control and vocabularies : an STM perspective / Iker Huerga and Michael P. Lauruhn
• A division of labor : the role of schema.org in a Semantic Web model of library resources / Carol Jean Godby
• BIBFRAME and linked data for libraries / Sally McCallum.

"With its roots in computer science, linked data is unfamiliar territory for many library catalogers. But since the origins of MARC nearly 50 years ago, the value of machine-readable library records has only grown. Today linked data is essential for sharing library collections on the open web, especially the digital cultural heritage in the collections of libraries, archives, and museums. In this book, the Association of Library Collections and Technical Services (ALCTS) gathers a stellar list of contributors to help readers understand linked data concepts by examining practice and projects based in familiar concepts like authority control. Topped by an insider's perspective on OCLC's experiments with Schema.org and the Library of Congress's BIBFRAME project, the book addresses such topics as: a simplified description of linked data, summing up its promises and challenges; controlled vocabularies for the web; broadening use of library-curated vocabularies; how the complexity of AV models reveals the limitations of retrospective conversion; BIBFRAME's triplestore data model; ways libraries are helping science researchers share their data, with descriptions of projects underway at major institutions; balancing the nuance within an element set with the sameness needed for sharing; and the influence of projects such as Europeana and Digital Public Library of America. This survey of the cultural heritage landscape will be a key resource for catalogers and those in the metadata community"--Publisher's website.

• Preface Library Standards and the Semantic Web Modeling Library Authority Files Modeling and Discovering Creative Works Entity Identification Through Text Mining The Library Linked Data Cloud Bibliography Authors' Biographies.
• (source: Nielsen Book Data)

This book describes OCLC's contributions to the transformation of the Internet from a web of documents to a Web of Data. The new Web is a growing `cloud' of interconnected resources that identify the things people want to know about when they approach the Internet with an information need. The linked data architecture has achieved critical mass just as it has become clear that library standards for resource description are nearing obsolescence. Working for the world's largest library cooperative, OCLC researchers have been active participants in the development of next-generation standards for library resource description. By engaging with an international community of library and Web standards experts, they have published some of the most widely used RDF datasets representing library collections and librarianship. 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 catalog, but elsewhere on the Internet. Modeling data in a form that the broader Web understands will project the value of libraries into the Digital Information Age. The exposition is aimed at librarians, archivists, computer scientists, and other professionals interested in modeling bibliographic descriptions as linked data. It aims to achieve a balanced treatment of theory, technical detail, and practical application.
(source: Nielsen Book Data)

• Introduction Preliminaries Cloud-Based RDF Storage Cloud-Based SPARQL Query Processing SPARQL Query Optimization for the Cloud RDFS Reasoning in the Cloud Concluding Remarks Bibliography Authors' Biographies.
• (source: Nielsen Book Data)

Resource Description Framework (or RDF, in short) is set to deliver many of the original semi-structured data promises: flexible structure, optional schema, and rich, flexible Universal Resource Identifiers as a basis for information sharing. Moreover, RDF is uniquely positioned to benefit from the efforts of scientific communities studying databases, knowledge representation, and Web technologies. As a consequence, the RDF data model is used in a variety of applications today for integrating knowledge and information: in open Web or government data via the Linked Open Data initiative, in scientific domains such as bioinformatics, and more recently in search engines and personal assistants of enterprises in the form of knowledge graphs. Managing such large volumes of RDF data is challenging due to the sheer size, heterogeneity, and complexity brought by RDF reasoning. To tackle the size challenge, distributed architectures are required. Cloud computing is an emerging paradigm massively adopted in many applications requiring distributed architectures for the scalability, fault tolerance, and elasticity features it provides. At the same time, interest in massively parallel processing has been renewed by the MapReduce model and many follow-up works, which aim at simplifying the deployment of massively parallel data management tasks in a cloud environment. In this book, we study the state-of-the-art RDF data management in cloud environments and parallel/distributed architectures that were not necessarily intended for the cloud, but can easily be deployed therein. After providing a comprehensive background on RDF and cloud technologies, we explore four aspects that are vital in an RDF data management system: data storage, query processing, query optimization, and reasoning. We conclude the book with a discussion on open problems and future directions.
(source: Nielsen Book Data)

• Introduction: RDF and Linked Open Data RDF Ecosystem Database technologies Native RDF storage approaches Non-native RDF storage approaches Conclusion.
• (source: Nielsen Book Data)

RDF Database Systems is a cutting-edge guide that distills everything you need to know to effectively use or design an RDF database. This book starts with the basics of linked open data and covers the most recent research, practice, and technologies to help you leverage semantic technology. With an approach that combines technical detail with theoretical background, this book shows how to design and develop semantic web applications, data models, indexing and query processing solutions.
(source: Nielsen Book Data)

• Preface Foreword by Phil Archer Foreword by Tom Baker Foreword by Dan Brickley and Libby Miller Acknowledgments Introduction The RDF Ecosystem Data Quality Shape Expressions SHACL Applications Comparing ShEx and SHACL Bibliography Authors' Biographies Index.
• (source: Nielsen Book Data)

RDF and Linked Data have broad applicability across many fields, from aircraft manufacturing to zoology. Requirements for detecting bad data differ across communities, fields, and tasks, but nearly all involve some form of data validation. This book introduces data validation and describes its practical use in day-to-day data exchange. The Semantic Web offers a bold, new take on how to organize, distribute, index, and share data. Using Web addresses (URIs) as identifiers for data elements enables the construction of distributed databases on a global scale. Like the Web, the Semantic Web is heralded as an information revolution, and also like the Web, it is encumbered by data quality issues. The quality of Semantic Web data is compromised by the lack of resources for data curation, for maintenance, and for developing globally applicable data models. At the enterprise scale, these problems have conventional solutions. Master data management provides an enterprise-wide vocabulary, while constraint languages capture and enforce data structures. Filling a need long recognized by Semantic Web users, shapes languages provide models and vocabularies for expressing such structural constraints. This book describes two technologies for RDF validation: Shape Expressions (ShEx) and Shapes Constraint Language (SHACL), the rationales for their designs, a comparison of the two, and some example applications.
(source: Nielsen Book Data)

• Table of Contents; Preface; Why Learn SPARQL?; Organization of This Book; Conventions Used in This Book; Documentation Conventions; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgments;
• Chapter 1. Jumping Right In: Some Data and Some Queries; The Data to Query; Querying the Data; More Realistic Data and Matching on Multiple Triples; Searching for Strings; What Could Go Wrong?; Querying a Public Data Source; Summary;
• Chapter 2. The Semantic Web, RDF, and Linked Data (and SPARQL); What Exactly Is the "Semantic Web"?; URLs, URIs, IRIs, and Namespaces

• The Resource Description Format (RDF)Storing RDF in Files; Storing RDF in Databases; Data Typing; Making RDF More Readable with Language Tags and Labels; Blank Nodes and Why They're Useful; Named Graphs; Reusing and Creating Vocabularies: RDF Schema and OWL; Linked Data; SPARQL's Past, Present, and Future; The SPARQL Specifications; Summary;
• Chapter 3. SPARQL Queries: A Deeper Dive; More Readable Query Results; Using the Labels Provided by DBpedia; Getting Labels from Schemas and Ontologies; Data That Might Not Be There; Finding Data That Doesn't Meet Certain Conditions

• Searching Further in the DataSearching with Blank Nodes; Eliminating Redundant Output; Combining Different Search Conditions; FILTERing Data Based on Conditions; Retrieving a Specific Number of Results; Querying Named Graphs; Queries in Your Queries; Combining Values and Assigning Values to Variables; Sorting, Aggregating, Finding the Biggest and Smallest and...; Sorting Data; Finding the Smallest, the Biggest, the Count, the Average...; Grouping Data and Finding Aggregate Values within Groups; Querying a Remote SPARQL Service; Federated Queries: Searching Multiple Datasets with One Query

• Datatype ConversionChecking, Adding, and Removing Spoken Language Tags; String Functions; Numeric Functions; Date and Time Functions; Hash Functions; Extension Functions; Summary;
• Chapter 6. Updating Data with SPARQL; Getting Started with Fuseki; Adding Data to a Dataset; Deleting Data; Changing Existing Data; Named Graphs; Dropping Graphs; Named Graph Syntax Shortcuts: WITH and USING; Deleting and Replacing Triples in Named Graphs; Summary;
• Chapter 7. Building Applications with SPARQL: A Brief Tour; SPARQL and Web Application Development; SPARQL Query Results XML Format; SPARQL Processors

"More and more people are using the query language SPARQL (pronounced 'sparkle') to pull data from a growing collection of public and private data. Whether this data is part of a semantic web project or an integration of two inventory databases on different platforms behind the same firewall, SPARQL is making it easier to access this data using both open source and commercial software. In the words of W3C Director and web inventor Tim Berners-Lee, 'Trying to use the Semantic Web without SPARQL is like trying to use a relational database without SQL. SPARQL lets them query information from databases and other diverse sources in the wild, across the Web.'"--Resource description page.

• Jumping right in : some data and some queries
• The Semantic Web, RDF, and Linked Data (and SPARQL)
• SPARQL queries : a deeper dive
• Copying, creating, and converting data (and finding bad data)
• Datatypes and functions
• Updating data with SPARQL
• Query efficiency and debugging
• Working with SPARQL query result formats
• RDF schema, OWL, and inferencing
• Building applications with SPARQL
• A SPARQL cookbook.

Get hands-on experience with SPARQL, the RDF query language. With this concise book, you will learn how to use the latest version of this W3C standard to retrieve and manipulate the increasing amount of public and private data available via SPARQL endpoints. Several open source and commercial tools already support SPARQL, and this introduction gets you started right away. Learn how to write and run simple SPARQL 1.1 queries, then dive into the language's powerful features and capabilities for manipulating the data you retrieve. Learn what you need to know to add to, update, and delete data in RDF datasets, and give web applications access to this data. Updated to reflect SPARQL 1.1. Recommendation version of specifications Understand SPARQL's connection with RDF, the semantic web, and related specifications Query and combine data from local and remote sources Copy, convert, and create new RDF data Learn how datatype metadata, standardized functions, and extension functions contribute to your queries New chapters cover SPARQL Query Results Formats, Query Efficiency and Debugging, RDF Schema, OWL, and Inferencing.Includes a cookbook of SPARQL queries useful in a variety of situations Expanded Application Development chapter.
(source: Nielsen Book Data)

• The Semantic Web and RDF: A Brief History
• The Specifications
• When to Use and Not Use RDF
• Some Uses of RDF/XML
• Related Technologies
• Going Forward
• RDF: Heart and Soul
• The Search for Knowledge
• The RDF Triple
• The Basic RDF Data Model and the RDF Graph
• URIs
• RDF Serialization: N3 and N-Triples
• Talking RDF: Lingo and Vocabulary
• The Basic Elements Within the RDF/XML Syntax
• Serializing RDF to XML
• RDF Blank Nodes
• URI References
• Representing Structured Data with rdf:value
• The rdf:type Property
• RDF/XML Shortcuts
• More on RDF Data Types
• RDF/XML: Separate Documents or Embedded Blocks
• Specialized RDF Relationships: Reification, Containers, and Collections
• Containers
• Collections
• Reification: The RDF Big Ugly
• Important Concepts from the W3C RDF Vocabulary/Schema
• RDF Vocabulary: Describing the Data
• Core RDF Schema Elements
• Refining RDF Vocabularies with Constraints
• RDF Schema Alternatives
• Creating an RDF Vocabulary
• How RDF Vocabularies Differ from XML Vocabularies
• Defining the Vocabulary: Business and Scope
• Defining the Vocabulary: Elements
• Formalizing the Vocabulary with RDFS
• Integrating the Dublin Core
• Editing, Parsing, and Browsing RDF/XML
• BrownSauce
• Parsers
• Editors
• Jena: RDF in Java
• Overview of the Classes
• Creating and Serializing an RDF Model
• Parsing and Querying an RDF Document
• In-Memory Versus Persistent Model Storage
• RDF and Perl, PHP, and Python
• RDF/XML and Perl
• RDF API for PHP.

The Resource Description Framework (RDF) is a structure for describing and interchanging metadata on the Web--anything from library catalogs and worldwide directories to bioinformatics, Mozilla internal data structures, and knowledge bases for artificial intelligence projects. RDF provides a consistent framework and syntax for describing and querying data, making it possible to share website descriptions more easily. RDF's capabilities, however, have long been shrouded by its reputation for complexity and a difficult family of specifications. Practical RDF breaks through this reputation with immediate and solvable problems to help you understand, master, and implement RDF solutions. Practical RDF explains RDF from the ground up, providing real-world examples and descriptions of how the technology is being used in applications like Mozilla, FOAF, and Chandler, as well as infrastructure you can use to build your own applications. This book cuts to the heart of the W3C's often obscure specifications, giving you tools to apply RDF successfully in your own projects. The first part of the book focuses on the RDF specifications. After an introduction to RDF, the book covers the RDF specification documents themselves, including RDF Semantics and Concepts and Abstract Model specifications, RDF constructs, and the RDF Schema. The second section focuses on programming language support, and the tools and utilities that allow developers to review, edit, parse, store, and manipulate RDF/XML. Subsequent sections focus on RDF's data roots, programming and framework support, and practical implementation and use of RDF and RDF/XML. If you want to know how to apply RDF to information processing, Practical RDF is for you. Whether your interests lie in large-scale information aggregation and analysis or in smaller-scale projects like weblog syndication, this book will provide you with a solid foundation for working with RDF.
(source: Nielsen Book Data)

• Preface Foreword by Phil Archer Foreword by Tom Baker Foreword by Dan Brickley and Libby Miller Acknowledgments Introduction The RDF Ecosystem Data Quality Shape Expressions SHACL Applications Comparing ShEx and SHACL Bibliography Authors' Biographies Index.
• (source: Nielsen Book Data)

RDF and Linked Data have broad applicability across many fields, from aircraft manufacturing to zoology. Requirements for detecting bad data differ across communities, fields, and tasks, but nearly all involve some form of data validation. This book introduces data validation and describes its practical use in day-to-day data exchange. The Semantic Web offers a bold, new take on how to organize, distribute, index, and share data. Using Web addresses (URIs) as identifiers for data elements enables the construction of distributed databases on a global scale. Like the Web, the Semantic Web is heralded as an information revolution, and also like the Web, it is encumbered by data quality issues. The quality of Semantic Web data is compromised by the lack of resources for data curation, for maintenance, and for developing globally applicable data models. At the enterprise scale, these problems have conventional solutions. Master data management provides an enterprise-wide vocabulary, while constraint languages capture and enforce data structures. Filling a need long recognized by Semantic Web users, shapes languages provide models and vocabularies for expressing such structural constraints. This book describes two technologies for RDF validation: Shape Expressions (ShEx) and Shapes Constraint Language (SHACL), the rationales for their designs, a comparison of the two, and some example applications.
(source: Nielsen Book Data)