By developing techniques and technologies that comprehensively assess genetic variation, cellular metabolism, and protein function, network medicine is opening up new vistas for uncovering causes and identifying cures of disease. A revolutionary new theory showing how we can predict human behavior.
Can we scientifically predict our future? Scientists and pseudo scientists have been pursuing this mystery for hundreds and perhaps thousands of years. But now, astonishing new research is revealing patterns in human behavior previously thought to be purely random.
Precise, orderly, predictable patterns His approach relies on the digital reality of our world, from mobile phones to the Internet and email, because it has turned society into a huge research laboratory. All those electronic trails of time stamped texts, voicemails, and internet searches add up to a previously unavailable massive data set of statistics that track our movements, our decisions, our lives. Analysis of these trails is offering deep insights into the rhythm of how we do everything.
We work and fight and play in short flourishes of activity followed by next to nothing. The pattern isn't random, it's "bursty. Bursts reveals what this amazing new research is showing us about where individual spontaneity ends and predictability in human behavior begins. The way you think about your own potential to do something truly extraordinary will never be the same. In the 's, James Gleick's Chaos introduced the world to complexity. We've long suspected that we live in a small world, where everything is connected to everything else. Indeed, networks are pervasive--from the human brain to the Internet to the economy to our group of friends.
These linkages, it turns out, aren't random. All networks have an underlying order and follow simple laws. Understanding the structure and behavior of these networks will help us do some amazing things, from designing the optimal organization of a firm to stopping a disease outbreak before it spreads catastrophically. These "new cartographers" are mapping networks in a wide range of scientific disciplines, proving that social networks, corporations, and cells are more similar than they are different, and providing important new insights into the interconnected world around us.
Engaging and authoritative, Linked provides an exciting preview of the next century in science, guaranteed to be transformed by these amazing discoveries. From the Internet to networks of friendship, disease transmission, and even terrorism, the concept--and the reality--of networks has come to pervade modern society. But what exactly is a network? What different types of networks are there? Why are they interesting, and what can they tell us? In recent years, scientists from a range of fields--including mathematics, physics, computer science, sociology, and biology--have been pursuing these questions and building a new "science of networks.
It is an ideal sourcebook for the key research in this fast-growing field. The book is organized into four sections, each preceded by an editors' introduction summarizing its contents and general theme. The first section sets the stage by discussing some of the historical antecedents of contemporary research in the area. From there the book moves to the empirical side of the science of networks before turning to the foundational modeling ideas that have been the focus of much subsequent activity.
The book closes by taking the reader to the cutting edge of network science--the relationship between network structure and system dynamics. From network robustness to the spread of disease, this section offers a potpourri of topics on this rapidly expanding frontier of the new science. Fractals and surfaces are two of the most widely-studied areas of modern physics.
In fact, most surfaces in nature are fractals. In this book, Drs. The authors begin by presenting basic growth models and the principles used to develop them. They next demonstrate how models can be used to answer specific questions about surface roughness. In the second half of the book, they discuss in detail two classes of phenomena: In each case, the authors review the model and analytical approach, and present experimental results.
This book is the first attempt to unite the subjects of fractals and surfaces, and it will appeal to advanced undergraduate and graduate students in condensed matter physics and statistical mechanics. The book discusses patterns for publishing Linked Data, describes deployed Linked Data applications and examines their architecture.
Please cite the book as:. Evolving the Web into a Global Data Space 1st edition. Synthesis Lectures on the Semantic Web: Theory and Technology, 1: The World Wide Web has enabled the creation of a global information space comprising linked documents. As the Web becomes ever more enmeshed with our daily lives, there is a growing desire for direct access to raw data not currently available on the Web or bound up in hypertext documents. Linked Data provides a publishing paradigm in which not only documents, but also data, can be a first class citizen of the Web, thereby enabling the extension of the Web with a global data space based on open standards - the Web of Data.
In this Synthesis lecture we provide readers with a detailed technical introduction to Linked Data. We begin by outlining the basic principles of Linked Data, including coverage of relevant aspects of Web architecture. The remainder of the text is based around two main themes - the publication and consumption of Linked Data. Drawing on a practical Linked Data scenario, we provide guidance and best practices on: We give an overview of existing Linked Data applications and then examine the architectures that are used to consume Linked Data from the Web, alongside existing tools and frameworks that enable these.
Readers can expect to gain a rich technical understanding of Linked Data fundamentals, as the basis for application development, research or further study. This book provides a conceptual and technical introduction to the field of Linked Data. It is intended for anyone who cares about data — using it, managing it, sharing it, interacting with it — and is passionate about the Web. We think this will include data geeks, managers and owners of data sets, system implementors and Web developers.
We hope that students and teachers of information management and computer science will find the book a suitable reference point for courses that explore topics in Web development and data management. Established practitioners of Linked Data will find in this book a distillation of much of their knowledge and experience, and a reference work that can bring this to all those who follow in their footsteps. Chapter 2 introduces the basic principles and terminology of Linked Data.
Chapter 3 provides a 30, ft view of the Web of Data that has arisen from the publication of large volumes of Linked Data on the Web. Chapter 4 discusses the primary design considerations that must be taken into account when preparing to publish Linked Data, covering topics such as choosing and using URIs, describing things using RDF, data licensing and waivers, and linking data to external data sets.
Chapter 5 introduces a number of recipes that highlight the wide variety of approaches that can be adopted to publish Linked Data, while Chapter 6 describes deployed Linked Data applications and examines their architecture. We would like to thank the series editors Jim Hendler and Frank van Harmelen for giving us the opportunity and the impetus to write this book.
Summarizing the state of the art in Linked Data was a job that needed doing — we are glad they asked us. Lastly, we would like to thank the developers of LaTeX and Subversion, without which this exercise in remote, collaborative authoring would not have been possible. We are surrounded by data — data about the performance of our locals schools, the fuel efficiency of our cars, a multitude of products from different vendors, or the way our taxes are spent.
Increasing numbers of individuals and organizations are contributing to this deluge by choosing to share their data with others, including Web-native companies such as Amazon and Yahoo! Third parties, in turn, are consuming this data to build new businesses, streamline online commerce, accelerate scientific progress, and enhance the democratic process. The strength and diversity of the ecosystems that have evolved in these cases demonstrates a previously unrecognised, and certainly unfulfilled, demand for access to data, and that those organizations and individuals who choose to share data stand to benefit from the emergence of these ecosystems.
This raises three key questions:. Just as the World Wide Web has revolutionized the way we connect and consume documents, so can it revolutionize the way we discover , access , integrate and use data. The Web is the ideal medium to enable these processes, due to its ubiquity, its distributed and scalable nature, and its mature, well-understood technology stack. The topic of this book is on how a set of principles and technologies, known as Linked Data , harnesses the ethos and infrastructure of the Web to enable data sharing and reuse on a massive scale.
In order to understand the concept and value of Linked Data, it is important to consider contemporary mechanisms for sharing and reusing data on the Web. A key factor in the re-usability of data is the extent to which it is well structured. The more regular and well-defined the structure of the data the more easily people can create tools to reliably process it for reuse.
While most Web sites have some degree of structure, the language in which they are created, HTML, is oriented towards structuring textual documents rather than data. As data is intermingled into the surrounding text, it is hard for software applications to extract snippets of structured data from HTML pages.watch
To address this issue, a variety of microformats 5 have been invented. Microformats can be used to published structured data describing specific types of entities, such as people and organizations, events, reviews and ratings, through embedding of data in HTML pages. As microformats tightly specify how to embed data, applications can unambiguously extract the data from the pages. Weak points of microformats are that they are restricted to representing data about a small set of different types of entities; they only provide a small set of attributes that may used to describe these entities; and that it is often not possible to express relationships between entities, such as, for example, that a person is the speaker of an event, rather than being just an attendee or the organizer of the event.
Therefore, microformats are not suitable for sharing arbitrary data on the Web. The advent of Web APIs has led to an explosion in small, specialized applications or mashups that combine data from several sources, each of which is accessed through an API specific to the data provider. While the benefits of programmatic access to structured data are indisputable, the existence of a specialized API for each data set creates a landscape where significant effort is required to integrate each novel data set into an application.
Every programmer must understand the methods available to retrieve data from each API, and write custom code for accessing data from each data source. However, from a Web perspective, they have some limitations, which are best explained by comparison with HTML. The HTML specification defines the anchor element, a , one of the valid attributes of which is the href. When used together, the anchor tag and href attribute indicate an outgoing link from the current document.
Web user agents , such as browsers and search engine crawlers, are programmed to recognize the significance of this combination, and either render a clickable link that a human user can follow, or to traverse the link directly in order to retrieve and process the referenced document. It is this connectivity between documents, supported by a standard syntax for indicating links, that has enabled the Web of documents. By contrast, the data returned from the majority of Web APIs does not have the equivalent of the HTML anchor tag and href attribute, to indicate links that should be followed to find related data.
Furthermore, many Web APIs refer to items of interest using identifiers that have only local scope — e. In such cases, there is no standard mechanism to refer to items described by one API in data returned by another. Consequently, data returned from Web APIs typically exists as isolated fragments, lacking reliable onward links signposting the way to related data. Therefore, while Web APIs make data accessible on the Web , they do not place it truly in the Web , making it linkable and therefore discoverable. To return to the comparison with HTML, the analogous situation would be a search engine that required a priori knowledge of all Web documents before it could assemble its index.
To provide this a priori knowledge, every Web publisher would need to register each Web page with each search engine. The same principles of linking, and therefore ease of discovery, can be applied to data on the Web, and Linked Data provides a technical solution to realize such linkage. Linking data distributed across the Web requires a standard mechanism for specifying the existence and meaning of connections between items described in this data. The key things to note at this stage are that RDF provides a flexible way to describe things in the world — such as people, locations, or abstract concepts — and how they relate to other things.
These statements of relationships between things are, in essence, links connecting things in the world. Therefore, if we wish to say that a book described in data from one API is for sale at a physical bookshop described in data from a second API, and that bookshop is located in a city described by data from a third, RDF enables us to do this, and publish this information on the Web in a form that others can discover and reuse. Therefore, a Web in which data is both published and linked using RDF is a Web where data is significantly more discoverable, and therefore more usable.
Linked Data: Evolving the Web into a Global Data Space
Just as hyperlinks in the classic Web connect documents into a single global information space, Linked Data enables links to be set between items in different data sources and therefore connect these sources into a single global data space. The use of Web standards and a common data model make it possible to implement generic applications that operate over the complete data space. This is the essence of Linked Data. Increasing numbers of data providers and application developers have adopted Linked Data. In doing so they have created this global, interconnected data space - the Web of Data.
Echoing the diversity of the classic document Web, the Web of Data spans numerous topical domains, such as people, companies, films, music, locations, books and other publications, online communities, as well as an increasing volume of scientific and government data. By enabling seamless connections between data sets, we can transform the way drugs are discovered, create rich pathways through diverse learning resources, spot previously unseen factors in road traffic accidents, and scrutinise more effectively the operation of our democratic systems.
The focus of this book is data sharing in the context of the public Web. However, the principles and techniques described can be equally well applied to data that exists behind a personal or corporate firewall, or that straddles the public and the private. For example, many aspects of Linked Data have been implemented in desktop computing environments through the Semantic Desktop initiative The Linking Open Drug Data  initiative represents a hybrid scenario, where Linked Data is enabling commercial organizations to connect and integrate data they are willing to share with each other for the purposes of collaboration.
X-Linked Adrenoleukodystrophy - GeneReviews® - NCBI Bookshelf
Information that changes rarely such as the company overview is published on the site as static HTML documents. Frequently changing information such as listing of productions is stored in a relational database and published to the Web site as HTML by a series of PHP scripts developed for the company. The term Linked Data refers to a set of best practices for publishing and interlinking structured data on the Web. These principles are the following:. In order to understand these Linked Data principles, it is important to understand the architecture of the classic document Web.
The document Web is built on a small set of simple standards: In addition, the Web is built on the idea of setting hyperlinks between Web documents that may reside on different Web servers. The development and use of standards enables the Web to transcend different technical architectures. Hyperlinks enable users to navigate between different servers.
They also enable search engines to crawl the Web and to provide sophisticated search capabilities on top of crawled content. Hyperlinks are therefore crucial in connecting content from different servers into a single global information space. By combining simplicity with decentralization and openness, the Web seems to have hit an architectural sweet spot, as demonstrated by its rapid growth over the past 20 years.
Linked Data builds directly on Web architecture and applies this architecture to the task of sharing data on global scale. The first Linked Data principle advocates using URI references to identify, not just Web documents and digital content, but also real world objects and abstract concepts. These may include tangible things such as people, places and cars, or those that are more abstract, such as the relationship type of knowing somebody , the set of all green cars in the world, or the color green itself.
This principle can be seen as extending the scope of the Web from online resources to encompass any object or concept in the world. In order to enable a wide range of different applications to process Web content, it is important to agree on standardized content formats.
The RDF data model is explained in more detail later in this chapter. The fourth Linked Data principle advocates the use of hyperlinks to connect not only Web documents, but any type of thing. For example, a hyperlink may be set between a person and a place, or between a place and a company. In contrast to the classic Web where hyperlinks are largely untyped, hyperlinks that connect things in a Linked Data context have types which describe the relationship between the things.
For example, a hyperlink of the type friend of may be set between two people, or a hyperlink of the type based near may be set between a person and a place. Hyperlinks in the Linked Data context are called RDF links in order to distinguish them from hyperlinks between classic Web documents. Just as hyperlinks in the classic Web connect documents into a single global information space, Linked Data uses hyperlinks to connect disparate data into a single global data space. These links, in turn, enable applications to navigate the data space.
For example, a Linked Data application that has looked up a URI and retrieved RDF data describing a person may follow links from that data to data on different Web servers, describing, for instance, the place where the person lives or the company for which the person works. As the resulting Web of Data is based on standards and a common data model, it becomes possible to implement generic applications that operate over the complete data space.
Examples of such applications include Linked Data browsers which enable the user to view data from one data source and then follow RDF links within the data to other data sources. Other examples are Linked Data Search engines that crawl the Web of Data and provide sophisticated query capabilities on top of the complete data space. In summary, the Linked Data principles lay the foundations for extending the Web with a global data space based on the same architectural principles as the classic document Web. The following sections explain the technical realization of the Linked Data principles in more detail.
To publish data on the Web, the items in a domain of interest must first be identified.
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These are the things whose properties and relationships will be described in the data, and may include Web documents as well as real-world entities and abstract concepts. The relationship, that they know each other, is represented by connecting lines having the relationship type http: If thinking about HTTP URIs as names for things rather than as addresses for Web documents feels strange to you, then references  and  are highly recommended reading and warrant re-visiting on a regular basis. Descriptions of resources are embodied in the form of Web documents. Descriptions that are intended to be read by humans are often represented as HTML.
Descriptions that are intended for consumption by machines are represented as RDF data. Where URIs identify real-world objects, it is essential to not confuse the objects themselves with the Web documents that describe them. It is, therefore, common practice to use different URIs to identify the real-world object and the document that describes it, in order to be unambiguous. This practice allows separate statements to be made about an object and about a document that describes that object.
For example, the creation date of a person may be rather different to the creation date of a document that describes this person. Being able to distinguish the two through use of different URIs is critical to the coherence of the Web of Data. The Web is intended to be an information space that may be used by humans as well as by machines. Both should be able to retrieve representations of resources in a form that meets their needs, such as HTML for humans and RDF for machines.
Servers can inspect these headers and select an appropriate response. There are two different strategies to make URIs that identify real-world objects dereferenceable. Both strategies ensure that objects and the documents that describe them are not confused, and that humans as well as machines can retrieve appropriate representations. The following sections summarize both strategies and illustrate each with an example HTTP session.
Real-world objects, like houses or people, can not be transmitted over the wire using the HTTP protocol. Thus, it is also not possible to directly dereference URIs that identify real-world objects. Therefore, in the URIs strategy, instead of sending the object itself over the network, the server responds to the client with the HTTP response code See Other and the URI of a Web document which describes the real-world object. This is called a redirect. In a second step, the client dereferences this new URI and gets a Web document describing the real-world object.
This process can be illustrated with a concrete example. This data should be understandable for humans as well as for machines. The client sends an Accept: The server would answer:. This is a redirect, which tells the client that a Web document containing a description of the requested resource, in the requested format, can be found at the URI given in the Location: Note that if the Accept: This is indicated by the Vary: Next, the client will try to dereference the URI given in the response from the server.
The status code tells the client that the response contains a representation of the requested resource. Only the beginning of this description is shown. The RDF data model, in general, will be described in 2. A widespread criticism of the URI strategy is that it requires two HTTP requests to retrieve a single description of a real-world object. One option for avoiding these two requests is provided by the hash URI strategy.
This special part is called the fragment identifier. This means a URI that includes a hash cannot be retrieved directly and therefore does not necessarily identify a Web document. In this fashion, URIs such as the following are created for the vocabulary terms:. First, the client truncates the URI, removing the fragment identifier e. Then, it connects to the server at biglynx. This demonstrates that the returned document contains not only a description of the vocabulary term http: The Linked Data-aware client will now inspect the response and find triples that tell it more about the http: If it is not interested in the triples describing the second resource, it can discard them before continuing to process the retrieved data.
So which strategy should be used? Both approaches have their advantages and disadvantages. The downside of the hash URI approach is that the descriptions of all resources that share the same non-fragment URI part are always returned to the client together, irrespective of whether the client is interested in only one URI or all.
If these descriptions consist of a large number of triples, the hash URI approach can lead to large amounts of data being unnecessarily transmitted to the client. There could be one describing document for each resource, or one large document for all of them, or any combination in between.
It is also possible to change the policy later on. As a result of these factors, URIs are often used to serve resource descriptions that are part of very large data sets, such as the description of an individual concept from DBpedia , an RDF-ized version of Wikipedia, consisting of 3. Hash URIs are often used to identify terms within RDF vocabularies, as the definitions of RDF vocabularies are usually rather small, maybe a thousand RDF triples, and as it is also often convenient for client applications to retrieve the complete vocabulary definition at once, instead of having to look up every term separately.
By using URIs that follow a http: RDF provides a data model that is extremely simple on the one hand but strictly tailored towards Web architecture on the other hand. To be published on the Web, RDF data can be serialized in different formats. This section gives an overview of the RDF data model, followed by a comparison of the different RDF serialization formats that are used in the Linked Data context. RDF aims at being employed as a lingua franca , capable of moderating between other data models that are used on the Web. Below, we give a short overview of the data model. In RDF, a description of a resource is represented as a number of triples.
The three parts of each triple are called its subject , predicate , and object. A triple mirrors the basic structure of a simple sentence, such as this one:. The subject of a triple is the URI identifying the described resource. The object can either be a simple literal value , like a string, number, or date; or the URI of another resource that is somehow related to the subject. The predicate, in the middle, indicates what kind of relation exists between subject and object, e.
The predicate is also identified by a URI. These predicate URIs come from vocabularies , collections of URIs that can be used to represent information about a certain domain. Please refer to Section 4. The URIs occurring as subject and object are the nodes in the graph, and each triple is a directed arc that connects the subject and the object. Linked Data applications operate on top of this giant global graph and retrieve parts of it by dereferencing URIs as required.
In order to make it easier for clients to consume data, it is recommended to use only the subset of the RDF data model described above. In particular, the following features should be avoided in a Linked Data context. It is important to remember that RDF is not a data format, but a data model for describing resources in the form of subject, predicate, object triples. This simply means taking the triples that make up an RDF graph, and using a particular syntax to write these out to a file either in advance for a static data set or on demand if the data set is more dynamic.
In addition several other non-standard serialization formats are used to fulfill specific needs. The relative advantages and disadvantages of the different serialization formats are discussed below, along with a code sample showing a simple graph expressed in each serialization. However, the syntax is also viewed as difficult for humans to read and write, and, therefore, consideration should be given to using other serializations in data management and curation workflows that involve human intervention, and to the provision of alternative serializations for consumers who may wish to eyeball the data.
The first one states that there is a thing, identified by the URI http: The second triple state that this thing has the name Dave Smith. RDFa is popular in contexts where data publishers are able to modify HTML templates but have relatively little additional control over the publishing infrastructure. For example, many content management systems will enable publishers to configure the HTML templates used to expose different types of information, but may not be flexible enough to support redirects and HTTP content negotiation. Turtle is a plain text format for serializing RDF data. Due to its support for namespace prefixes and various other shorthands, Turtle is typically the serialization format of choice for reading RDF triples or writing them by hand.
N-Triples is a subset of Turtle, minus features such as namespace prefixes and shorthands. The result is a serialization format with lots of redundancy, as all URIs must be specified in full in each triple. However, this redundancy is also the primary advantage of N-Triples over other serialization formats, as it enables N-Triples files to be parsed one line at a time, making it ideal for loading large data files that will not fit into main memory.
The redundancy also makes N-Triples very amenable to compression, thereby reducing network traffic when exchanging files. These two factors make N-Triples the de facto standard for exchanging large dumps of Linked Data, e. Such external RDF links are fundamental for the Web of Data as they are the glue that connects data islands into a global, interconnected data space and as they enable applications to discover additional data sources in a follow-your-nose fashion.
Dereferencing these URIs yields a description of the linked resource provided by the remote server. This description will usually contain additional RDF links which point to other URIs that, in turn, can also be dereferenced, and so on. This is how individual resource descriptions are woven into the Web of Data.
This is also how the Web of Data can be navigated using a Linked Data browser or crawled by the robot of a search engine. There are three important types of RDF links:. The following section gives examples of all three types of RDF link and discusses their role on the Web of Data. The Web of Data contains information about a multitude of things ranging from people, companies, and places, to films, music, books, genes, and various other types of data.
Chapter 3 will give an overview of the data sources that currently make up the Web of Data. RDF links enable references to be set from within one data set to entities described in another, which may, in turn, have descriptions that refer to entities in a third data set, and so on. Therefore, setting RDF links not only connects one data source to another, but enables connections into a potentially infinite network of data that can be used collectively by client applications.
By following this link, applications can find population counts, postal codes, descriptions in 90 languages, and lists of famous people and bands that are related to Birmingham. The rationale for, and implications of, this can be illustrated with an example of someone who will be known as Jeff who wants to publish data on the Web describing himself.
Jeff must first define a URI to identify himself, in a namespace that he owns, or in which the domain name owner has allowed him to create new URIs. After looking up the URI and receiving the descriptive data, an information consumer knows two things: But what happens if Jeff wants to publish data describing a location or a famous person on the Web?
The same procedure applies: Jeff defines URIs identifying the location and the famous person in his namespace and serves the data when somebody looks up these URIs. In an open environment like the Web it is likely that Jeff is not the only one talking about the place or the famous person, but that there are many different information providers who talk about the same entities.
As they all use their own URIs to refer to the person or place, the result is multiple URIs identifying the same entity. In order to still be able to track the different information providers speak about the same entity, Linked Data relies on setting RDF links between URI aliases. By common agreement, Linked Data publishers use the link type http: To use different URIs to refer to the same entity and to use owl: The reasons for this are:. The last point becomes especially clear when one considers the size of many data sets that are part of the Web of Data.
For instance, the Geonames data set provides information about over eight million locations. If in order to start publishing their data on the Web of Data, the Geonames team would need to find out what the commonly accepted URIs for all these places would be, doing so would be so much effort that it would likely prevent Geonames from publishing their dataset as Linked Data at all.
Later, they, or somebody else, may invest effort into finding and publishing owl: Therefore, in contrast to relying on upfront agreement on URIs, the Web of Linked Data relies on solving the identity resolution problem in an evolutionary and distributed fashion: There has been significant uncertainty in recent years about whether owl: Therefore, we recommend to also use owl: The promise of the Web of Data is not only to enable client applications to discover new data sources by following RDF links at run-time but also to help them to integrate data from these sources.
Integrating data requires bridging between the schemata that are used by different data sources to publish their data. The term schema is understood in the Linked Data context as the mixture of distinct terms from different RDF vocabularies that are used by a data source to publish data on the Web. This mixture may include terms from widely used vocabularies see Section 4. On the one hand side, it tries to avoid heterogeneity by advocating the reuse of terms from widely deployed vocabularies.
As discussed in Section 4. Thus, whenever these vocabularies already contain the terms needed to represent a specific data set, they should be used. This helps to avoid heterogeneity by relying on ontological agreement. On the other hand, the Web of Data tries to deal with heterogeneity by making data as self-descriptive as possible. Technically, this is realized in a twofold manner: Together these techniques enable Linked Data applications to discover the meta-information that they need to integrate data in a follow-your-nose fashion along RDF links. Linked Data publishers should therefore adopt the following workflow: Wherever possible, the publisher should seek wider adoption for the new, proprietary vocabulary from others with related data.
If a looser mapping is desired, then rdfs: The example below illustrates how the proprietary vocabulary term http: The more links that are set between vocabulary terms, the better client applications can integrate data that is represented using different vocabularies. This type of data integration is discussed in more detail in Section 6.
This chapter has outlined the basic principles of Linked Data and has described how the principles interplay in order to extend the Web with a global data space. Similar to the classic document Web, the Web of Data is built on a small set of standards and the idea to use links to connect content from different sources. The extent of its dependence on URIs and HTTP demonstrates that Linked Data is not disjoint from the Web at large, but simply an application of its principles and key components to novel forms of usage.
Far from being an additional layer on top of but separate from the Web, Linked Data is just another warp or weft being steadily interwoven with the fabric of the Web. Structured data is made available on the Web today in forms. Data is published as CSV data dumps, Excel spreadsheets, and in a multitude of domain-specific data formats. Various data providers have started to allow direct access to their databases via Web APIs. So what is the rationale for adopting Linked Data instead of, or in addition to, these well-established publishing techniques?
In summary, Linked Data provides a more generic, more flexible publishing paradigm which makes it easier for data consumers to discover and integrate data from large numbers of data sources. In particular, Linked Data provides:. Compared to the other methods of publishing data on the Web, these properties of the Linked Data architecture make it easier for data consumers to discover, access and integrate data.
However, it is important to remember that the various publication methods represent a continuum of benefit, from making data available on the Web in any form, to publishing Linked Data according to the principles described in this chapter. Progressive steps can be taken towards Linked Data publishing, each of which make it easier for third parties to consume and work with the data.
These steps include making data available on the Web in any format but under an open license, to using structured, machine-readable formats that are preferably non-proprietary, to adoption of open standards such as RDF, and to inclusion of links to other data sources.
Crucially, each rating can be obtained in turn, representing a progressive transition to Linked Data rather than a wholesale adoption in one operation. The Web of Data can be seen as an additional layer that is tightly interwoven with the classic document Web and has many of the same properties:. The founding aim of the project, which has spawned a vibrant and growing Linked Data community, was to bootstrap the Web of Data by identifying existing data sets available under open licenses, convert them to RDF according to the Linked Data principles, and to publish them on the Web.
As a point of principle, the project has always been open to anyone who publishes data according to the Linked Data principles. This openness is a likely factor in the success of the project in bootstrapping the Web of Data. Each node in the diagram represents a distinct data set published as Linked Data. The arcs indicate the existence of links between items in the two data sets.
Heavier arcs correspond to a greater number of links, while bidirectional arcs indicate that outward links to the other exist in each data set. The graphic shown in this figure is available online at http: Updated versions of the graphic will be published on this website in regular intervals.
If you publish a linked data set yourself, please also add it to this catalog so that it will be included into the next version of the cloud diagram. Instructions on how to add data sets to the catalog are found in the ESW wiki This section gives an overview of the topology of the Web of Data as of November Data sets are classified into the following topical domains: The number of RDF links refers to out-going links that are set from data sources within a domain to other data sources.
Some of the first data sets that appeared in the Web of Data are not specific to one topic, but span multiple domains. This cross-domain coverage is crucial for helping to connect domain-specific data sets into a single, interconnected data space, thereby avoiding fragmentation of the Web of Data into isolated, topical data islands. RDF statements that refer to this URI are then generated by extracting information from various parts of the Wikipedia articles, in particular the infoboxes commonly seen on the right hand side of Wikipedia articles.
Because of its breadth of topical coverage, DBpedia has served as a hub within the Web of Data from the early stages of the Linking Open Data project. The wealth of inward and outward links connecting items in DBpedia to items in other data sets is apparent in Figure 3. A second major source of cross-domain Linked Data is Freebase 24 , an editable, openly-licensed database populated through user contributions and data imports from sources such as Wikipedia and Geonames.
Freebase provides RDF descriptions of items in the database, which are linked to items in DBpedia with incoming and outgoing links. These are, in turn, linked with DBpedia, helping to facilitate data integration across a wide range of interlinked sources.
Geography is another factor that can often connect information from varied topical domains. This is apparent in the Web of Data, where the Geonames 27 data set frequently serves as a hub for other data sets that have some geographical component. Geonames is an open-license geographical database that publishes Linked Data about 8 million locations.
Wherever possible, locations in Geonames and LinkedGeoData are interlinked with corresponding locations in DBpedia, ensuring there is a core of interlinked data about geographical locations. Linked Data versions of the EuroStat 28 , World Factbook 29 and US Census 30 data sets begin to bridge the worlds of statistics, politics and social geography, while Ordnance Survey the national mapping agency of Great Britain has begun to publish Linked Data describing the administrative areas within the Great Britain 31 , in efforts related to the data.
One of the first large organisations to recognise the potential of Linked Data and adopt the principles and technologies into their publishing and content management workflows has been the British Broadcasting Corporation BBC. Following earlier experiments with publishing their catalogue of programmes as RDF, the BBC released in two large sites that combine publication of Linked Data and conventional Web pages. This music data is interlinked with DBpedia, and it receives incoming links from a range of music-related Linked Data sources.
These cross-data set links allow applications to consume data from all these sources and integrate it to provide rich artist profiles, while the playlist data can be mined to find similarities between artists that may be used to generate recommendations. More recently, the BBC have launched the site Wildlife Finder 34 , which presents itself to users as a conventional Web site with extensive information about animal species, behaviours and habitats. Outgoing links connect each species, behaviour and habitat to the corresponding resources in the DBpedia data set, and to BBC Programmes that depict these.
In this case, the goal of using RDF was not to expose Linked Data for consumption by third parties, but to aid internal content management and data integration in a domain with high levels of connectivity between players, teams, fixtures and stadia. Elsewhere in the media sector, there have also been significant moves towards Linked Data by major players. The New York Times has published a significant proportion of its internal subject headings as Linked Data 37 under a Creative Commons Attribution license see Section 4. The intention is to use this liberally-licensed data as a map to lead people to the rich archive of content maintained by the New York Times.
Services such as this are particularly significant for their ability to bridge Linked Data and conventional hypertext documents, potentially allowing documents such as blog posts or news articles to be enhanced with relevant pictures or background data. Governmental bodies and public-sector organisations produce a wealth of data, ranging from economic statistics, to registers of companies and land ownership, reports on the performance of schools, crime statistics, and the voting records of elected representatives.
Recent drives to increase government transparency, most notably in countries such as Australia 39 , New Zealand 40 , the U. Making this data easily accessible enables organisations and members of the public to work with the data, analyse it to discover new insights, and build tools that help communicate these findings to others, thereby helping citizens make informed choices and hold public servants to account.
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The potential of Linked Data for easing the access to government data is increasingly understood, with both the data. The approach taken in the two countries differs slightly: Further high-level guidance on "Putting Government Data online" can be found in . In order to provide a forum for coordinating the work on using Linked Data and other Web standards to improve access to government data and increase government transparency, W3C has formed a eGovernment Interest Group With an imperative to support novel means of discovery, and a wealth of experience in producing high-quality structured data, libraries are natural complementors to Linked Data.
This field has seen some significant early developments which aim at integrating library catalogs on a global scale; interlinking the content of multiple library catalogs, for instance, by topic, location, or historical period; interlink library catalogs with third party information picture and video archives, or knowledge bases like DBpedia ; and at making library data easier accessible by relying on Web standards. Similarly, the OpenLibrary , a collaborative effort to create "one Web page for every book ever published" 50 publishes its catalogue in RDF, with incoming links from data sets such as ProductDB see Section 3.
An application that facilitates this scholarly data space is Talis Aspire The application supports educators in the creation and management of literature lists for university courses. Items are added to these lists through a conventional Web interface; however, behind the scenes, the system stores these records as RDF and makes the lists available as Linked Data. Aspire is used by various universities in the UK, which, in turn, have become Linked Data providers.
The Aspire application is explored in more detail in Section 6. High levels of ongoing activity in the library community will no doubt lead to further significant Linked Data deployments in this area. The adoption of this model by libraries, museums and cultural institutions that participate in Europeana will further accelerate the availability of Linked Data related to publications and cultural heritage artifacts. In order to provide a forum and to coordinate the efforts to increase the global interoperability of library data, W3C has started a Library Linked Data Incubator Group Linked Data has gained significant uptake in the Life Sciences as a technology to connect the various data sets that are used by researchers in this field.
The Book Mashup uses the Simple Commerce Vocabulary 61 to represent and republish data about book offers retrieved from the Amazon. GoodRelations has seen significant uptake from retailers such as Best Buy 63 and Overstock. The adoption of the GoodRelations ontology has even extended to the publication of price lists for courses offered by The Open University The ProductDB Web site and data set 66 aggregates and links data about products for a range of different sources and demonstrates the potential of Linked Data for the area of product data integration.
Some of the earliest data sets in the Web of Data were based on conversions of, or wrappers around, Web 2. This has produced data sets and services such as DBpedia and the FlickrWrappr 67 , a Linked Data wrapper around the Flickr photo-sharing service. These were complemented by user-generated content sites that were built with native support for Linked Data, such as Revyu. There are several hundred publicly accessible Semantic MediaWiki installations 71 that publish their content to the Web of Data.
More recently, Linked Data principles and technologies have been adopted by major players in the user-generated content and social media spheres, the most significant example of which is the development and adoption by Facebook of the Open Graph Protocol This enables Facebook to more easily consume data from sites across the Web, as it is published at source in structured form. Within a few months of its launch, numerous major destination sites on the Web, such as the Internet Movie Database 73 , had adopted the Open Graph Protocol to publish structured data describing items featured on their Web pages.
The primary challenge for the Open Graph Protocol is to enable a greater degree of linking between data sources, within the framework that has already been well established. Another area in which RDFa is enabling the publication of user-generated content as Linked Data is through the Drupal content management system The data sets described in this chapter demonstrate the diversity in the Web of Data. Recently published data sets, such as Ordnance Survey , legislation.
This trend is expected to gather significant momentum, with organisations in other industry sectors publishing their own data according to the Linked Data principles. Linked Data is made available on the Web using a wide variety of tools and publishing patterns. This chapter will discuss the primary design considerations that must be taken into account when preparing data to be published as Linked Data on the Web, before introducing specific publishing recipes in Chapter 5.
These design considerations are not about visual design, but about how one shapes and structures data to fit neatly in the Web. They break down into three areas, each of which maps onto one or two of the Linked Data principles: The outcome of these design decisions contributes directly to the utility and usability of a set of Linked Data, and therefore ultimately its value to the people and software programs that use it.
As discussed in Chapter 2 , the first principle of Linked Data is that URIs should be used as names for things that feature in your data set. These things might be concrete real-world entities such as a person, a building, your dog, or more abstract notions such as a scientific concept. Each of these things needs a name so that you and others can refer to it. Just as significant care should go into the design of URIs for pages in a conventional Web site, so should careful decisions be made about the design of URIs for a set of Linked Data.
This section will explore these issues in detail. This allows these names to be looked up using any client, such as a Web browser, that speaks the HTTP protocol. In practical terms, using http: Therefore, they are free to mint URIs in this namespace to use as names for things they want to talk about. As discussed in Chapter 1 , a primary reason for publishing Linked Data is to add value through creation of incoming and outgoing links. Therefore, to help inspire confidence in third parties considering linking to a data set, some effort should be expended on minting stable, persistent URIs for entities in that data set.
The specifics of the technical hosting environment may introduce some constraints on the precise syntax of these URIs; however, the following simple rules should be followed to help achieve this:. Where a particular Web site is seen as authoritative in a particular domain, and it provides stable URIs for entities in this domain or pages about those entities , it can be very tempting to try and misappropriate these URIs for use in a Linked Data context.
Each is described in a document at an address such as:. It is not unreasonable at first glance to consider augmenting this URI with a fragment identifier to create a URI that identifies the film itself, rather than a document about the film, such as:. However, this approach is problematic as no one other than the owner of the imdb.
If IMDb adopted the Linked Data principles it would constitute a highly appropriate target for such linking. However, this is not the case at the time of writing, and therefore alternatives such as DBpedia and LinkedMDB 76 should be considered. Wherever possible, URIs should not reflect implementation details that may need to change at some point in the future. For example, including server names or other indicators of underlying technical infrastructure in URIs is undesirable.
In the case of Big Lynx , whose site is hosted on a machine called tiger and implemented mostly in PHP, the following is considered uncool as a potential URI for an RDF document containing data about Dave Smith, as it includes both the name of the machine and the. In contrast, the URI below could be considered cool, as it is less likely to break if the site is moved to a different machine or is reimplemented using a different scripting language or framework:. To ensure the uniqueness of URIs it is often useful to base them on some existing primary key, such as a unique product ID in a database table.
In the case of Big Lynx , the company is small enough that a combination of given name and family name can ensure uniqueness of URIs for members of staff, as shown in the examples below. This has the advantage of creating a more human-readable and memorable URI. In a larger organisation, an employee ID number may provide a suitable alternative. A good general principle is to, wherever possible, use a key that is meaningful within the domain of the data set.
For the sake of example and if we ignore for the moment the issues with non-uniqueness of ISBNs , using the ISBN as part of the URI for a book is preferable to using its primary key from your internal database. This makes it significantly easier to link your book data with that from other sources as there is a common key on which links can be based.
Linking bibliographic works, including the use of natural versus articifical keys, is discussed in more detail in . References  and  provide background information on the topic of minting Cool URIs and are recommended reading.
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Each entity represented in a particular data set will likely lead to the minting of at least three URIs, as discussed in Section 2. This can be problematic for developers new to Linked Data concepts, as they may not realise that the URI in a browser address bar has changed following content negotation and a redirect, and inadvertently refer to the wrong URI. This form has the advantage that the various URIs are more visually distinct due to the use of different subdomains.
From an system architectural perspective, this may also simplify the Linked Data publication process by allowing RDF descriptions of resources to be served by a D2R Server described in Section 5. Scripts at the id subdomain would simply be responsible for performing content negotiation and redirects. Assuming that a URI has been minted for each entity in a data set, according to the guidelines above, the next consideration concerns what information to provide in response when someone looks up that URI. Let us assume that we have a data set expressed as RDF triples.
Which of these triples should be included in the RDF description of a particular resource? The list below enumerates the various types of triples that should be included into the description:.