semantic web Concepts

Mastering Structured Data on the Semantic Web: From HTML5 Microdata to Linked Open Data

by Leslie Sikos · 10 Jul 2015

145 ■Chapter ■ 7: Querying�� 173 ■Chapter ■ 8: Big Data Applications�� 199 ■Chapter ■ 9: Use Cases�� 217 Index�� 227 iii Chapter 1 Introduction to the Semantic Web The content of conventional web sites is human-readable only, which is unsuitable for automatic processing and inefficient when searching for related information. Web datasets

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ontology file. Web ontologies make it possible to describe complex statements in any topic in a machine-readable format. The architecture of the Semantic Web is illustrated by the “Semantic Web Stack,” which shows the hierarchy of standards in which each layer relies on the layers below (see Figure 1-5). 5 Chapter

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1 ■ Introduction to the Semantic Web Figure 1-5. The Semantic Web Stack While the preceding data formats are primarily machine-readable, they can be linked from human-readable web pages or integrated into human

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lifeboat.com/ex/web.3.0. Accessed 16 March 2015. 6. Herman, I. (ed.) (2009) How would you define the main goals of the Semantic Web? In: W3C Semantic Web FAQ. World Wide Web Consortium. www.w3.org/2001/sw/SW-FAQ#swgoals. Accessed 18 January 2015. 7. Sbodio, L. M., Martin, D.,

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A Three-Day Conference Represented in hCalendar <link rel="profile" href="http://microformats.org/profile/hcalendar" /> … <div class="vevent"> <h1 class="summary">Semantic Web Conference 2015</h1> <div class="description">Semantic Web Conference 2015 was announced yesterday.</div> <div>Posted on: <abbr class="dtstamp" title="20150825T080000Z">Aug 25, 2015</abbr></div> Beyond microformats such

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my web site</a>. I am the author of <a property="fabio:Textbook"  href="http://lesliesikos.com/mastering-structured-data-on-the-semantic-web/">Mastering  Structured Data on the Semantic Web</a>. To make search engines “understand” that the provided link refers to a textbook of Leslie Sikos, we used the machine-readable

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metadata. Software tools can extract structured data from properly written semantic documents and display them arbitrarily. This is the true essence of the Semantic Web ! 110 Chapter 4 ■ Semantic Web Development Tools A useful feature of Sindice Web Data Inspector is that a scalable graph can be generated from your semantic document. The

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powerful that its developers integrated the framework with CKAN, the LOD cloud metadata registry to generate timely and comprehensive statistics about the LOD cloud. Semantic Web Browsers Semantic Web browsers are browsing tools for exploring and visualizing RDF datasets enhanced with Linked Data such as machine-readable definitions from DBpedia or geospatial information

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unrelated operations are not supported. Web Service Modeling Ontology (WSMO) The Web Service Modeling Ontology (WSMO, pronounced “Wizmo”) is a conceptual model for Semantic Web Services, covering the core Semantic Web Service elements as an ontology using the WSML formal description language and the WSMX execution environment [8]. WSMO is derived from and based

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resolve possible representation mismatches between ontologies, mediators that link web services to goals, and mediators that link two web services. The Semantic Web Service descriptions can 133 Chapter 5 ■ Semantic Web Services cover functional and usage descriptions. The functional descriptions describe the capabilities of the service, while the usage description describes the interface

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Software Developers can use semantic execution environments such as WSMX and IRS to provide automatic discovery, composition, selection, mediation, and invocation of Semantic Web Services. The development of Semantic Web Services can be speeded up using purpose-built frameworks and plug-ins such as the Web Services Modeling Toolkit (WSMT) and the Semantic

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-code/. Contents About the Author�� xiii About the Technical Reviewer��xv Preface��xvii ■Chapter ■ 1: Introduction to the Semantic Web �� 1 The Semantic Web�� 1 Structured Data�� 2 Semantic Web Components�� 5 Ontologies�� 6 Inference�� 7 Semantic Web Features�� 7 Free, Open Access Data Repositories�� 8 Adaptive Information�� 8 Unique Web Resource Identifiers�� 8 Summary�� 9 References

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70 Licensing�� 71 vi ■ Contents RDF Statements�� 72 Interlinking�� 72 Registering Your Dataset�� 74 Linked Data Visualization�� 75 Summary�� 76 References�� 77 ■Chapter ■ 4: Semantic Web Development Tools�� 79 Advanced Text Editors�� 79 Semantic Annotators and Converters�� 81 RDFa Play�� 82 RDFa 1.1 Distiller and Parser�� 82 RDF Distiller�� 83

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113 Tabulator�� 113 Marbles�� 114 OpenLink Data Explorer (ODE)�� 114 DBpedia Mobile�� 116 IsaViz�� 116 RelFinder�� 117 Summary�� 117 References�� 117 ■Chapter ■ 5: Semantic Web Services�� 121 Semantic Web Service Modeling�� 121 Communication with XML Messages: SOAP�� 122 Web Services Description Language (WSDL)�� 124 Web Ontology Language for Services (OWL-S)�� 129 Web Service

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Modeling Ontology (WSMO)�� 133 viii ■ Contents Web Service Modeling Language (WSML)�� 138 Web Services Business Process Execution Language (WS-BPEL)�� 140 Semantic Web Service Software�� 141 Web Service Modeling eXecution environment (WSMX)�� 141 Internet Reasoning Service (IRS-III)�� 141 Web Services Modeling Toolkit (WSMT)�� 141 Semantic Automated Discovery

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and Integration (SADI)�� 142 UDDI Semantic Web Service Listings�� 142 Summary�� 142 References�� 143 ■Chapter ■ 6: Graph Databases�� 145 Graph Databases�� 145 Triplestores�� 149 Quadstores�� 149 The Most Popular Graph Databases

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193 4store SPARQL Server�� 195 PublishMyData�� 195 Summary�� 197 References�� 197 ■Chapter ■ 8: Big Data Applications�� 199 Big Semantic Data: Big Data on the Semantic Web�� 199 Google Knowledge Graph and Knowledge Vault�� 200 Get Your Company, Products, and Events into the Knowledge Graph�� 202 Social Media Applications�� 205 Facebook Social

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Performance Storage: The One Trillion Triples Mark�� 213 Summary�� 214 References�� 215 ■Chapter ■ 9: Use Cases�� 217 RDB to RDF Direct Mapping�� 217 A Semantic Web Service Process in OWL-S to Charge a Credit Card�� 221 Modeling a Travel Agency Web Service with WSMO�� 223 Querying DBpedia Using the RDF

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API of Jena�� 224 Summary�� 225 References �� 226 Index�� 227 xi About the Author Leslie F. Sikos, Ph.D., is a Semantic Web researcher at Flinders University, South Australia, specializing in semantic video annotations, ontology engineering, and natural language processing using Linguistic Linked Open Data. On the cutting

Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the Agi Workshop 2006

by Ben Goertzel and Pei Wang · 1 Jan 2007 · 303pp · 67,891 words

. López de Mántaras, R. Mizoguchi, M. Musen and N. Zhong Volume 157 Recently published in this series Vol. 156. R.M. Colomb, Ontology and the Semantic Web Vol. 155. O. Vasilecas et al. (Eds.), Databases and Information Systems IV – Selected Papers from the Seventh International Baltic Conference DB&IS’2006 Vol. 154

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knowledge from its original format into Narsese. x The Internet. It is possible for NARS to be equipped with additional modules, which use techniques like semantic web, information retrieval, and data mining, to directly acquire certain knowledge from the Internet, and put them into Narsese. x Natural language interface. After NARS has

Business Metadata: Capturing Enterprise Knowledge

by William H. Inmon, Bonnie K. O'Neil and Lowell Fryman · 15 Feb 2008 · 314pp · 94,600 words

processes and the rigid information management models used by business applications. Look to the future: next generation business intelligence, enterprise content management and search, the semantic web all will depend on business metadata. Read this book!” —David Loshin, President, Knowledge Integrity Incorporated These authors have written a book that ventures into new

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Metadata Delivery 188 Summary 192 References 194 Chapter 11 Semantics and Business Metadata 11.1 11.2 11.3 Introduction 195 The Vision of the Semantic Web 195 The Importance of Semantics 196 Semantics Are Context-Sensitive 197 195 xvi Complete Table of Contents 11.4 11.5 11.6 11.7

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businesspeople and technical people alike. Although we will discuss traditional metadata technology, we will also venture into technology subjects such as: ✦ Web 2.0 ✦ “The Semantic Web” ✦ Collaboration and Groupware ✦ “The Wisdom of Crowds” ✦ Data Management and Governance The proper exploitation of business metadata, by both technologists and businesspeople, can revolutionize the

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. Westridge Consulting, 2001 and 2006. Semantics and Business Metadata 1. 2. 3. 4. 5. 6. 7. 8. 11.1 Introduction ................................................................................................195 The Vision of the Semantic Web .....................................................195 The Importance of Semantics ..........................................................196 Attempts to Capture Semantics: Semantic Frameworks .................................................................................................200 Semantics as Business Metadata ....................................................207 Semantics in Practice .............................................................................211 Summary .......................................................................................................216 References

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P T E R TA B L E O F CO N T E N T S The Vision of the Semantic Web Tim Berners-Lee envisioned the idea of the “semantic web,” wherein intelligent agents would be truly intelligent. 195 196 Chapter 11 Semantics and Business Metadata In his vision the computer would

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home, book a reservation for you, and put it automatically on your calendar, all without human intervention. In the context of searching for documents, a semantic web would be able to understand what the documents contained. Today, we rely mostly on document titles and tagging. Tagging is usually done manually either by

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, someone else charged with tagging after the fact, or through a folksonomy like del.icio.us. But a true semantic web could decipher document contents on its own. On a smaller scale, the semantic web means distinguishing between word senses: when there are two or more senses of a word, the user is asked

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integration, 211–212 National Cancer Institute semantic vocabulary implementation, 214–216 service-oriented architecture, 214 Web Services, 212–213 prospects for integration and discovery, 280 semantic Web, 195–196 spectrum, 200–201, 208 Semistructured data, examples and technologies, 222–223 Serial transfer, knowledge management, 267–268 Service-oriented architecture (SOA) integrated metadata

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, 225 word counting, 226 Value/frequency report, data profiling, 181, 184–186 Web 2.0 knowledge capture folksonomy, 118–119 mashups, 115–116 overview, 115 semantic Web, 195–196 Web Ontology Language (OWL), semantic framework, 204–205, 210 Web Services, semantics interface, 212–213 Wiki governance, 106 knowledge capture, 104 limitations, 105

Learning SPARQL

by Bob Ducharme · 15 Jul 2011 · 315pp · 70,044 words

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 it. In the

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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 was not designed to query relational data, but to query data conforming

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running a few simple queries before getting into more detail on the background and use of SPARQL. Chapter 2, The Semantic Web, RDF, and Linked Data (and SPARQL) The bigger picture: the semantic web, related specifications, and what SPARQL adds to and gets out of them. Chapter 3, SPARQL Queries: A Deeper Dive

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Brief Tour How you can incorporate SPARQL queries into web-based applications. Glossary A glossary of terms and acronyms used when discussing SPARQL and the semantic web. You’ll also find an index at the back of the book to help you quickly locate explanations for SPARQL and RDF keywords and concepts

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and tested and rewrote and rewrote this. Chapter 1. Jumping Right In: Some Data and Some Queries Chapter 2 provides some background on RDF, the semantic web, and where SPARQL fits in, but before going into that, let’s start with a bit of hands-on experience writing and running SPARQL queries

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represents. A vocabulary of property names typically has its own namespace to make it easier to use it with other sets of data. Note In semantic web development, a vocabulary is a set of terms stored using a standard format that people can reuse. When we revise the sample data to use

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describe how to create more complex queries, how to modify data, how to build applications around your queries, and how it all fits into the semantic web, but if you can execute the queries shown in this chapter, you’re ready to put SPARQL to work for you. Chapter 2. The

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the model—it’s about the data. The booming amount of data becoming available on the semantic web is making great new kinds of applications possible, and as a well-implemented, mature standard designed with the semantic web in mind, SPARQL is the best way to get that data and put it to work

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in your applications. What Exactly Is the “Semantic Web”? As excitement over the semantic web grows, some vendors use the phrase to sell products with strong connections

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to the ideas behind the semantic web, and others use it to sell products with weaker connections. This can be confusing

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for people trying to understand the semantic web landscape. I like to define the semantic web as a set of standards and best practices for sharing data and the semantics of that data over the web for use by applications.

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and especially web pages), and his system grew to become the biggest one ever. Berners-Lee founded the W3C to oversee these standards, and the semantic web is also built on W3C standards: the RDF data model, the SPARQL query language, and the RDF Schema and OWL standards for storing vocabularies and

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product or project may deal with semantics, but if it doesn’t use these standards, it can’t connect to and be part of the semantic web any more than a 1985 hypertext system could link to a page on the World Wide Web without using the HTML or HTTP standards. (There

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to name things and the use of standards such as RDF and SPARQL. They provide excellent guidelines for the creation of an infrastructure for the semantic web. and the semantics of that data The idea of “semantics” is often defined as “the meaning of words.” Linked Data principles and the related standards

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“buy,” we know more about the resources that have these properties and the relationships between these resources. Let’s look at these components of the semantic web in more detail. URLs, URIs, IRIs, and Namespaces When Berners-Lee invented the Web, along with writing the first web server and browser, he developed

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if Bridget’s father is Peter and Peter’s father is Henry, then Bridget’s grandfather is Henry. Inferencing often plays an important role in semantic web applications. N3 never became a standard, and no one really uses these extra features because they inspired separate work at the W3C that did become

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: <http://www.w3.org/2000/01/rdf-schema#> . <http://dbpedia.org/resource/Switzerland> rdfs:label "Switzerland"@en, "Suiza"@es, "Sveitsi"@fi, "Suisse"@fr . Note When semantic web applications retrieve information in response to a query, it’s very common for them to retrieve the rdfs:label values associated with the relevant resources

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was using an object-oriented system, I’d have to declare a new musician instance and then assign it all the details about Richard. Using semantic web standards, by adding one property to the metadata about the existing resource ab:i0432, that resource becomes a member of a class that it wasn

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without also supporting at least some of OWL. This ability of RDF resources to become members of classes based on their data values has made semantic web technology popular in areas such as medical research and intelligence agencies. Researchers can accumulate data with little apparent structure and then see what structure turns

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OWL-aware software will watch for so that it can make inferences from the data that use them. Without defining a large, complex ontology, many semantic web developers use just a few classes and properties from OWL to add metadata to their triples. For example, how much information do you think the

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Richard, Craig, and Cindy) we got more out of this dataset than we originally put into it. This is one of the great payoffs of semantic web technology. Tip The OWL 2 upgrade to the original OWL standard introduced several profiles, or subsets of OWL, that are specialized for certain kinds of

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OWL 2 RL, OWL 2 QL, and OWL 2 EL as possible starting points for your needs. Of all the W3C semantic web standards, OWL is the key one for putting the “semantic” in “semantic web.” The term “semantics” is sometimes defined as the meaning behind words, and those who doubt the value of

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this fact was not part of the dataset. Linked Data The idea of Linked Data is newer than that of the semantic web, but sometimes it’s easier to think of the semantic web as building on the ideas behind Linked Data. Linked Data is not a specification, but a set of best practices

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for providing a data infrastructure that makes it easier to share data across the web. You can then use semantic web technologies such as RDFS, OWL, and SPARQL to build applications around that data. Tim Berners-Lee came up with these four principles of Linked Data

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Polytechnic Institute converted a lot of the simpler data that they found through the US Data.gov project to RDF so that they could build semantic web applications around it. After seeing this work, US CIO Vivek Kundra appointed Hendler the “Internet Web Expert” for Data.gov. Tip The term “Linked Open

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0 users who want to get the most out of 1.1 quickly. Summary In this chapter, we learned: What the semantic web is. Why URIs are the foundation of the semantic web, their relationship to URLs and IRIs, and the role of namespaces. How people store RDF, and how they can identify the

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to let you get more out of the data they describe. How Linked Data is a popular set of best practices for sharing data that semantic web applications can build on, and what kind of data is becoming available. SPARQL’s history and the specifications that make up the SPARQL standard.

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sometimes have new facts that you can store. Creating new data from existing data is one of the most exciting aspects of SPARQL and the semantic web. Converting Data: If your application expects data to fit a certain model, and you have data that almost but not quite fits that model,

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two relational databases so that you can search for new relationships between table rows from the different databases is much easier said than done. In semantic web and Linked Data applications, the combination of two datasets like this is very common; easy data aggregation is one of RDF’s greatest benefits. Combining

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to v:homeTel may not seem like much of a change, but remember the URIs that those prefixes stand for. Lots of software in the semantic web world will recognize the predicate http://www.w3.org/2006/vcard/ns#email, but nothing outside of what I’ve written for this book will

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trouble. If the data conforms to a proper schema, the developer using the data doesn’t have to write code to account for that possibility. Semantic web applications take a different approach. Instead of providing a template that data must fit into so that processing applications can make assumptions about the data

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or not. More often, though, if a resource’s data breaks any rules, you’ll want to know which resources broke which rules. If a semantic web application checked for data that broke certain rules and then let you know which problems it found and where, how would it represent this information

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that it’s a string, it’s more likely to be an identifier such as a postal code or a part number. Decades before the semantic web, the storing of datatype metadata was one of the earliest ways to record semantic information. Knowing this extra bit of information about a piece of

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developing a standardized mapping to improve consistency between the use of different relational systems in different semantic web application environments. Note The Oracle Corporation’s most well-known products are relational database managers, and plenty of semantic web applications have middleware like D2RQ serving up triples created from the relational data in these products

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of triples that a SPARQL processor should retrieve from a dataset. See Also triple patternSPARQL processor inferencing Deriving additional facts from existing information. In a semantic web application, this often means creating new triples based on logic applied to existing ones; RDFS and OWL provide additional possibilities. See Also OWL IRI Internationalized

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by applications. Because these best practices recommend the use of URIs and standardized data formats, data that follows these practices is easier to use with semantic web technology. literal A value, as opposed to a URI, which is a name for something. A literal may have a datatype or a spoken

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be the string “Weaving the Web”. See Also subjectpredicateliteralblank node ontology This term can mean different things to different people, especially philosophers, but in the semantic web world, ontologies are formal definitions of vocabularies that allow you to define classes of resources, resource properties, and relationships between resource class members. OWL A

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technology, a schema is a set of rules about structure and datatypes used for validation to ensure data quality and more efficient systems. In the semantic web world, the RDF Schema (RDFS) specification lets you specify classes, properties, and metadata about those classes and properties. These serve as metadata to let you

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popular vocabulary for storing business card information such as someone’s first name, family name, email address, and job title. See Also vocabulary vocabulary In semantic web development, a set of terms stored using a standard format that people can reuse. RDF Schema and OWL are the key formats for doing this

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data, finding, Finding Bad Data, Using Existing SPARQL Rules Vocabularies BASE, Node Type Conversion Functions Berners-Lee, Tim, Why Learn SPARQL?, What Exactly Is the “Semantic Web”?, Linked Data Linked Data and, Linked Data biggest value, finding, Finding the Smallest, the Biggest, the Count, the Average..., Finding the Smallest, the Biggest, the

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DBpedia LCASE(), String Functions LIMIT, Retrieving a Specific Number of Results, Federated Queries: Searching Multiple Datasets with One Query Linked Data, What Exactly Is the “Semantic Web”?, Linked Data, Linked Data, Linked Data, Public Endpoints, Private Endpoints, Public Endpoints, Private Endpoints, Glossary intranets and, Public Endpoints, Private Endpoints Linked Open Data, Linked

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, Data That Might Not Be There Oracle, Storing RDF in Databases, Middleware SPARQL Support ORDER BY, Sorting Data OWL, What Exactly Is the “Semantic Web”?, What Exactly Is the “Semantic Web”?, Reusing and Creating Vocabularies: RDF Schema and OWL, Reusing and Creating Vocabularies: RDF Schema and OWL, Reusing and Creating Vocabularies: RDF Schema and

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, Querying the Data R rand(), Numeric Functions RDF, The Data to Query, The Resource Description Format (RDF), Named Graphs RDF Schema, What Exactly Is the “Semantic Web”?, Reusing and Creating Vocabularies: RDF Schema and OWL, Reusing and Creating Vocabularies: RDF Schema and OWL, Reusing and Creating Vocabularies: RDF Schema and OWL, Linked

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Data to Query (see RDF) round(), Numeric Functions S sample code, Using Code Examples schema, What Exactly Is the “Semantic Web”?, Glossary Schemarama, Using Existing SPARQL Rules Vocabularies screen scraping, What Exactly Is the “Semantic Web”?, Storing RDF in Files, Glossary searching for string, Searching for Strings SELECT, Querying the Data, Query Forms: SELECT

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, DESCRIBE, ASK, and CONSTRUCT semantic web, What Exactly Is the “Semantic Web”? semantics, What Exactly Is the “Semantic Web”?, Reusing and Creating Vocabularies: RDF Schema and OWL semicolon, Storing RDF in Files, More Readable Query Results, Converting Data, Named Graphs

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Realistic Data and Matching on Multiple Triples, Glossary W W3C, Jumping Right In: Some Data and Some Queries, What Exactly Is the “Semantic Web”? Web Ontology Language, What Exactly Is the “Semantic Web”? (see OWL) wget utility, SPARQL and Web Application Development WHERE, Querying the Data white space in queries, Querying the Data WITH

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About the Author Bob DuCharme (http://www.snee.com/bob) is a solutions architect at TopQuadrant, a provider of software for modeling, developing, and deploying semantic web applications. He came to TopQuadrant from Innodata Isogen, where he did system and architecture analysis and design for a wide range of global publishing clients

The Stack: On Software and Sovereignty

by Benjamin H. Bratton · 19 Feb 2016 · 903pp · 235,753 words

likely solution along with tools for the User to accomplish that intention as part of the search result. These are techniques sometimes associated with the semantic web, for which structured data are linked and associated to allow instrumental relations with other data, making the web as a whole more programmable by Users

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efficacy or accuracy. Just as most of the traffic on the Internet today is machine-to-machine, or at least machine generated, so too a semantic web of things21 would be correlated less by the cognitive dispositions or instrumental intentions of human Users, but those of “objects” and other instances within the

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. Payam Barnaghi, Cory Henson, Kerry Taylor, and Wei Wang, “Semantics for the Internet of Things: Early Progress and Back to the Future,” International Journal on Semantic Web and Information System 8, no. 1 (2012): 1–21, http://knoesis.org/library/download/IJSWIS_SemIoT.pdf. 22. Yann Moulier-Boutang, Cognitive Capitalism (London: Polity

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as a nascent form of an artificial human personality. We are invited not only to interact with iOS, and through the operating system with the semantic web (or at least the parts of the web that Siri knows how to search and process), but also to interact with Siri herself. The development

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machines, like a North Korean stadium pageant without an actual country behind it, all decisions linked by an ontological proletariat writing the rules of proprietary semantic webs. If everyone (in principle) has the right of exit and to opt out of their citizenship end user agreement for another offered elsewhere, but all

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, 261 self-knowledge through numbers, 261 self-mapping swarms, 265 self-realization, 129 self-reflection of the User, 252–253 semantics of the address, 193 semantic web, 202–203 “sensing like a state,” 340 sensing networks, 303 sensors blanketing Earth, 97, 180, 192, 198, 295 design questions, 342 forming a Cloud of

Beautiful Architecture: Leading Thinkers Reveal the Hidden Beauty in Software Design

by Diomidis Spinellis and Georgios Gousios · 30 Dec 2008 · 680pp · 157,865 words

, the browser seemed like the only real client to serve. As we started to realize the applicability of persistent, unambiguous identifiers for use in the Semantic Web, life sciences, publication, and similar communities, we knew that it was time to rethink the architecture to be more useful for both people and software

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2007 meeting was how to approach searching and semantic linking. The KDE 4 platform was gaining powerful solutions for pervasive indexing, rich metadata handling, and semantic webs with the Strigi and Nepomuk projects, which could yield very interesting possibilities when integrated with Akonadi. It was unclear whether a component feeding data into

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, and commercial domains. He has designed and built network matrix switch control systems, online games, 3D simulation/visualization environments, Internet-distributed computing platforms, P2P, and Semantic Web-based systems. He has a B.S. in computer science from the College of William and Mary and currently lives in Fairfax, Virginia. He is

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the president of Bosatsu Consulting, Inc., a professional services company focused on web architecture, resource-oriented computing, the Semantic Web, advanced user interfaces, scalable systems, security consulting, and other technologies of the late 20th and early 21st centuries. Diomidis Spinellis is an Associate Professor in

Text Analytics With Python: A Practical Real-World Approach to Gaining Actionable Insights From Your Data

by Dipanjan Sarkar · 1 Dec 2016

formally denoted and represented by semantic data models using graph structures, where concepts or entities are the nodes and the edges denote the relationships. The Semantic Web is as extension of the World Wide Web using semantic metadata annotations and embeddings using data-modeling techniques like Resource Description Framework (RDF) and Web

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key phrases. This technique falls under the broad umbrella of information retrieval and extraction. Keyphrase extraction finds its uses in many areas, including the following: Semantic web Query-based search engines and crawlers Recommendation systems Tagging systems Document similarity Translation Keyphrase extraction is often the starting point for carrying out more complex

Clojure Programming

by Chas Emerick, Brian Carper and Christophe Grand · 15 Aug 2011 · 999pp · 194,942 words

the var-dereferencing line noise. [179] Or, returned by a previous expansion. [180] Triples are a term for subject-predicate-object expressions, as found in semantic web technologies like RDF. Specific representations and semantics of triples vary from implementation to implementation, but a simplified example of a vector triple might be ["Boston

Data Mining: Concepts and Techniques: Concepts and Techniques

by Jiawei Han, Micheline Kamber and Jian Pei · 21 Jun 2011

modeling 316 from DBLP data set 316–317 effectiveness 317 example 314–315 of frequent patterns 313–317 mutual information 315–316 task definition 315 Semantic Web 597 semi-offline materialization 226 semi-supervised classification 432–433, 437 alternative approaches 433 cotraining 432–433 self-training 432 semi-supervised learning 25 outlier

Principles of Web API Design: Delivering Value with APIs and Microservices

by James Higginbotham · 20 Dec 2021 · 283pp · 78,705 words

" } } } } } * * * Semantic Hypermedia Messaging Semantic hypermedia messaging is the most comprehensive category as it adds semantic profile and linked data support, making APIs part of the Semantic Web. By applying semantics of resource properties through linked data, more meaning is assigned to each property without requiring an explicit name to be used. Linked

This Is for Everyone: The Captivating Memoir From the Inventor of the World Wide Web

by Tim Berners-Lee · 8 Sep 2025 · 347pp · 100,038 words

Engineering Security

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The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences

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Learning SPARQL

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Monadic Design Patterns for the Web

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Graph Databases

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RDF Database Systems: Triples Storage and SPARQL Query Processing

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AI in Museums: Reflections, Perspectives and Applications

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Artificial Intelligence: A Modern Approach

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The Tangled Web: A Guide to Securing Modern Web Applications

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Website Optimization

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Content Everywhere: Strategy and Structure for Future-Ready Content

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Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

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Peer-to-Peer

by Andy Oram · 26 Feb 2001 · 673pp · 164,804 words

Data Mining: Concepts, Models, Methods, and Algorithms

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The Boy Who Could Change the World: The Writings of Aaron Swartz

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Intertwingled: The Work and Influence of Ted Nelson (History of Computing)

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Cataloging the World: Paul Otlet and the Birth of the Information Age

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Mining the Social Web: Finding Needles in the Social Haystack

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Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage

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The Cultural Logic of Computation

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Beautiful Visualization

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The Future of the Brain: Essays by the World's Leading Neuroscientists

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Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

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