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

Learning SPARQL

by Bob Ducharme · 22 Jul 2011 · 511pp · 111,423 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 Building

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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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complex queries, how to modify data, how to build applications around your queries, the potential role of inferencing, and the technology’s roots in the semantic web world, 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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Semantic Web, RDF, and Linked Data (and SPARQL) The SPARQL query language is for data that follows a particular model, but the semantic web isn’t about the query language or about the model—it’s about the data

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. The booming amount of data becoming available on the semantic web is making great new kinds of applications possible, and as a well-implemented

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, 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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and more with projects that have nothing to do with the “semantic web” other than their use of technology that uses these standards—that’s why you’ll often see references to “semantic web technology.” What Exactly Is the “Semantic Web”? As excitement over the semantic web grows, some vendors use the phrase to sell products with strong

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

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be confusing 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. Let

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especially web pages), and his system grew to become the biggest hypertext system 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 used these extra features because they inspired separate work at the W3C that did become

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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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, because these profiles are designed to make it easier to implement large-scale systems for particular domains. 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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semantic web technology like to question the viability of storing all the meaning of a word in a machine-readable way. As we saw above, though, we

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more about RDFS and OWL in Chapter 9. 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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(and all other W3C standards and drafts) at http://www.w3.org/TR/. 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 Chapter

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’s more likely to be an identifier such as a postal code, the identifier of an ISO standard, 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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create something more powerful than RDFS but easier to implement than any of the OWL flavors described. In Dean Allemang and Jim Hendler’s book Semantic Web for the Working Ontologist (Morgan Kaufmann, 2011), they describe a superset that they call RDFS+, a spec that has been implemented in TopQuadrant’s TopBraid

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“Weaving the Web”. See Also subject, predicate, literal, blank 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. See Also

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’s developers found. Linked Data principles provide ways to share data on the Web that reduce the need for screen scraping. See Also Linked Data. semantic web A set of standards and best practices for sharing data and the semantics of that data over the Web for use by applications. The key

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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 and, Linked Data biggest value, finding, Finding the Smallest, the Biggest, the Count, the Average...–Finding the Smallest, the Biggest, the Count, the

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LCASE(), String Functions, Discussion 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, Problem, Glossary intranets and, Public Endpoints, Private Endpoints Linked Open Data, Linked Data, Public Endpoints, Private Endpoints Linked Movie Database, SPARQL

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RDF in Databases, Middleware SPARQL Support ORDER BY, Sorting Data outer join, Data That Might Not Be There 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, Linked Data, What Is Inferencing?, Applications and

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R2RML, Middleware SPARQL Support rand(), Numeric Functions RDF, The Data to Query, The Resource Description Framework (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, Linked Data Model-driven development and, Model-Driven Development

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Application Development rules, SPARQL (see SPARQL rules) S sameTerm(), Node Type and Datatype Checking Functions sample code, Using Code Examples schema, What Exactly Is the “Semantic Web”?, Glossary querying, Querying Schemas Schemarama, Using Existing SPARQL Rules Vocabularies Schematron, Finding Bad Data screen scraping, What Exactly Is the

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the Search Space searching for string, Searching for Strings SELECT, Querying the Data, Query Forms: SELECT, DESCRIBE, ASK, and CONSTRUCT semantic web, What Exactly Is the “Semantic Web”?, Glossary semantics, What Exactly Is the “Semantic Web”?, Reusing and Creating Vocabularies: RDF Schema and OWL semicolon, More Readable Query Results connecting operations with, Named Graphs CONSTRUCT queries

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Matching on Multiple Triples, Glossary VoID RDF schema, Themes and Variations W W3C, Jumping Right In: Some Data and Some Queries, What Exactly Is the “Semantic Web”? Web Ontology Language (see OWL) wget utility, SPARQL and Web Application Development, SPARQL and HTTP WHERE, Querying the Data whitespace in queries, Querying the Data

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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

Monadic Design Patterns for the Web

by L.G. Meredith · 214pp · 14,382 words

Contexts and URIs, Oh My! 7. A Review of Collections as Monads 8. Domain Model, Storage, and State 9. Putting it All Together 10. The Semantic Web Glossary Bibliography About the Author Cover · Overview · Contents · Discuss · Suggest · Glossary · Index vii x xi xii xiii 16 36 55 76 94 115 143 175

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Our web application end-to-end . . . . . . . 9.3 Deploying our application . . . . . . . . . . 9.4 From one web application to web framework 9.5 Foundations . . . . . . . . . . . . . . . . . 10 The Semantic Web 10.1 Practice . . . . . . . . . . . . 10.2 Referential transparency . . . 10.3 Composing monads . . . . . 10.4 Semantic application queries . 10.5 Searching for programs . . . 10.6 Foundations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

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collections. • Chapter 8, “Domain Model, Storage, and State,” looks at the storage model. • Chapter 9, “Putting It All Together,” investigates application deployment. • Chapter 10, “The Semantic Web,” addresses new foundations for semantic query. Cover · Overview · Contents · Discuss · Suggest · Glossary · Index 32 Section 1.3 Chapter 1 · Motivation and Background Download from Wow

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to web framework TBD Download from Wow! eBook <www.wowebook.com> 9.5 Foundations Cover · Overview · Contents · Discuss · Suggest · Glossary · Index 180 Chapter 10 The Semantic Web Where are we; how did we get here; and where are we going? Chapter 10 query model Chapter 6 Chapter 1 request stream browser Chapter

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10.1 · Chapter 10 map Cover · Overview · Contents · Discuss · Suggest · Glossary · Index Download from Wow! eBook <www.wowebook.com> Section 10.1 Chapter 10 · The Semantic Web 10.1 Practice 10.2 Referential transparency In the interest of complete transparency, it is important for me to be clear about my position on

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the current approach to the semantic web. As early as 2004 i appeared in print as stating a complete lack of confidence regarding meta-data, tags and ontology-based approaches. Despite the

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ground the introduction of new apparatus in good use cases. The Cover · Overview · Contents · Discuss · Suggest · Glossary · Index 182 Section 10.3 Chapter 10 · The Semantic Web discussion above can be turned directly into a use case. The central point of this chapter is to develop a query language for searching for

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had a way of swapping the interior G F to make Cover · Overview · Contents · Discuss · Suggest · Glossary · Index 183 Section 10.3 Chapter 10 · The Semantic Web 184 it F G, that is, we had a map of the form d : G F => F G (d for distributive because it distributes F

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this in terms of two extremely simple monads, a DSL for forming arithCover · Overview · Contents · Discuss · Suggest · Glossary · Index Section 10.3 Chapter 10 · The Semantic Web metic expressions involving only addition, i.e. a monoid, and a monad for collection, in this case Set. Download from Wow! eBook <www.wowebook.com

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( for( a <- s1 ; b <- s2 ) yield { MMExpr( List( a, b ) ) } ) case ... } Cover · Overview · Contents · Discuss · Suggest · Glossary · Index 185 Section 10.4 Chapter 10 · The Semantic Web 186 This is exactly the type we want. 10.4 Semantic application queries An alternative presentation If you recall, there’s an alternative way to

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very compactly as [[true]] = L [[¬c]] = L\c [[c&d]] = [[c]] ∩ [[d]] Cover · Overview · Contents · Discuss · Suggest · Glossary · Index Section 10.4 Chapter 10 · The Semantic Web 187 Now, what’s happening when we pull the monoid monad through the set monad via a distributive map is this. First, the monoid monad

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get the disjunction, ||, by the usual DeMorgan translation: c||d = ¬(¬c&¬d) Cover · Overview · Contents · Discuss · Suggest · Glossary · Index Section 10.4 Chapter 10 · The Semantic Web 188 i.e. ¬(true ∗ true). This is a little overkill, however. We just want to eliminate non-trivial compositions. We know how to express the

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construction of Boolean disjunction. This is, in fact, another kind of disjunction. Cover · Overview · Contents · Discuss · Suggest · Glossary · Index Section 10.4 Chapter 10 · The Semantic Web In some sense, the story here, much like the Sherlock Holmes story, is that the dog didn’t bark. The patterns we calculate from our

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more recently the process calculi, like Milner’s π-calculus or Cover · Overview · Contents · Discuss · Suggest · Glossary · Index 189 Section 10.4 Chapter 10 · The Semantic Web 190 of the specification of a language, makes it possible to factor code that handles a wide range of semantic features. The logic we derive

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as Processes, where he reformulated the presentation π-calculus along these lines. Cover · Overview · Contents · Discuss · Suggest · Glossary · Index Section 10.4 Chapter 10 · The Semantic Web • for( _( fixpt ) <- d if (( f ) => ((x) => f (x(x)))((x) => f (x(x)))) (true) ) yield fixpt • for( a <- d if h(x) => ((Y f )x

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∈ [[d]].m0 (m) → m00 , m00 ∈ [[c]]} Other collection monads, other logics Cover · Overview · Contents · Discuss · Suggest · Glossary · Index 191 Section 10.5 Chapter 10 · The Semantic Web 192 Stateful collections Other logical operations EXPRESSION PREVIOUS QUANTIFICATION FIXPT DEFN c, d ::= | ... | ∀v.c | rec X.c FIXPT MENTION |X 10.5 Searching for

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laws Examples Download from Wow! eBook <www.wowebook.com> 10.6 Foundations Cover · Overview · Contents · Discuss · Suggest · Glossary · Index Section 10.6 Chapter 10 · The Semantic Web 193 data1 dataK { form1 } constraint1 constraintN formK Download from Wow! eBook <www.wowebook.com> form { form : form1 <- data1,..., formK <- dataK, constraint1, ,..., constraintN } Figure 10.2

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

When Computers Can Think: The Artificial Intelligence Singularity

by Anthony Berglas, William Black, Samantha Thalind, Max Scratchmann and Michelle Estes · 28 Feb 2015

does understand. As advances are made in commonsense reasoning this may change. Producing an effective natural language query processor is a major goal of the semantic web community. Eurisko and other early results One of the more commonly quoted early works is Eurisko, created by Douglas Lenat in 1976. It used various

AI in Museums: Reflections, Perspectives and Applications

by Sonja Thiel and Johannes C. Bernhardt · 31 Dec 2023 · 321pp · 113,564 words

Recognition Letters 133, 102–08. https://doi.org/10.1016/j.patrec.2020.02.017. Foka, Anna/Attemark, Jenny/Wahlberg, Fredrik (2022). Women’s Metadata, Semantic Web, Ontologies and AI: Potentials in Critically Enriching Carl Sahlin’s Industrial History Collection. In: Theopisti Stylianou-Lambert/Alexandra Bounia/ Antigone Heraclidou (Eds.). Emerging Technologies and

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

by Zdravko Markov and Daniel T. Larose · 5 Apr 2007

.T.L. CONTENTS PREFACE xi PART I WEB STRUCTURE MINING 1 2 INFORMATION RETRIEVAL AND WEB SEARCH 3 Web Challenges Web Search Engines Topic Directories Semantic Web Crawling the Web Web Basics Web Crawlers Indexing and Keyword Search Document Representation Implementation Considerations Relevance Ranking Advanced Text Search Using the HTML Structure in

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. There are also approaches to do this automatically by applying machine learning methods for classiﬁcation and clustering. We look into these approaches in Part II. Semantic Web Semantic web is a recent initiative led by the web consortium (w3c.org). Its main objective is to bring formal knowledge representation techniques into the Web. Currently

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nice format of web pages is very difﬁcult for computers to understand—something that we expect search engines to do. The main idea behind the semantic web is to add formal descriptive material to each web page that although invisible to people would make its content easily understandable by computers. Thus, the

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give explanations. The web consortium site (http://www.w3.org/2001/sw/) provides detailed information about the latest developments in the area of the semantic web. Although the semantic web is probably the future of the Web, our focus is on the former two approaches to bring semantics to the Web. The reason for

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this is that web search is the data mining approach to web semantics: extracting knowledge from web data. In contrast, the semantic web approach is about turning web pages into formal knowledge structures and extending the functionality of web browsers with knowledge manipulation and reasoning tools. 6 CHAPTER

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quality indexing and keyword search, 13–32. See also Indexing and keyword search similarity search, 36–42. See also Similarity search web challenges, 3–5 semantic web, 5 topic directories, 5 web growth, 3 web search engines, 4 Jaccard similarity, 38–41 k-nearest-neighbor (k-NN), 119 distance-weighted, 120 Laplace

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

Content Everywhere: Strategy and Structure for Future-Ready Content

by Sara Wachter-Boettcher · 28 Nov 2012 · 245pp · 68,420 words

content’s needs against them and the more you can participate in conversations with those on the database end of the spectrum. What About the Semantic Web? Once you understand a bit about markup, and about making content machine-readable and interoperable, then it’s time to consider some of the exciting

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stuff that markup makes possible. One of those things is the Semantic Web: a Web where all content shares a common framework and can be shared, reused, and understood across systems—to the point where, say, machines know

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whether the term “blackberry” is referring to the fruit or the phone. A completely semantic Web is a lofty goal—one not without its detractors, I might note—and our path toward it is still meandering at best. But a more

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semantic Web seems closer than ever with the recent advent of linked data, which is made possible through structured content and markup. Coined by Tim Berners-Lee—

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pages and page types, and instead think purely about the mental model of the subject you’re trying to represent. How do linked data and Semantic Web fit in? Where once we built ourselves silos on the Web, these days it pays to recognize that it’s really one Web and we

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’re in the business of stitching our content into that wider canvas. Initiatives like the Linked Open Data and Semantic Web projects are helping us do this by providing standardized methods of sharing data for both people and computers. For example, dbPedia and MusicBrainz provide free

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, that failure has finally caught up with us. It’s time we right the ship. Wherever the world goes with markup, whatever happens with the Semantic Web and APIs and even big hairy problems like media revenue models, the truth remains: You’re going to need content that’s ready for multiple

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optimization (SEO), 124, 127 tactics gaming system, 196 search engines common language for, 100 findability for, 123–125 semantic markup, 97–98, 99–104, 140 Semantic Web, 102–104, 127 SFGate.com, 176, 177 shared attribute, for content hubs, 124 sharing content, 178 shopping, APIs for, 113 sidebar element, 46 sidebars, 79

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

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

RDF Database Systems: Triples Storage and SPARQL Query Processing

by Olivier Cure and Guillaume Blin · 10 Dec 2014

Artificial Intelligence: A Modern Approach

by Stuart Russell and Peter Norvig · 14 Jul 2019 · 2,466pp · 668,761 words

Clojure Programming

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

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