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1 What Is the Semantic Web?

This book is about something we call the Semantic Web. From the name, you can probably guess that it is related somehow to the World Wide Web (WWW) and that it has something to do with semantics. Semantics, in turn, has to do with understanding the nature of meaning, but even the word semantics has a number of meanings. In what sense are we using the word semantics? And how can it be applied to the Web?

This book is for a working ontologist. An ontologist might do their work as part of an Enterprise Knowledge Graph, a data lake, global linked data, graph data, or any of a number of other technological approaches that share the idea that data is more powerful when it comes together in a meaningful way. The aim of this book is not to motivate or pitch the Semantic Web but to provide the tools necessary for working with it. Or, perhaps more accurately, the World Wide Web Consortium (W3C) has provided these tools in the forms of standard Semantic Web languages, complete with abstract syntax, model-based semantics, reference implementations, test cases, and so forth. But these are like any tools—there are some basic tools that are all you need to build many useful things, and there are specialized craftsman’s tools that can produce far more specialized outputs. Whichever tools are needed for a particular task, however, one still needs to understand how to use them. In the hands of someone with no knowledge, they can produce clumsy, ugly, barely functional output, but in the hands of a skilled craftsman, they can produce works of utility, beauty, and durability. It is our aim in this book to describe the craft of building Semantic Web systems. We go beyond only providing a coverage of the fundamental tools to also show how they can be used together to create semantic models, sometimes called ontologies or vocabularies, that are understandable, useful, durable, and perhaps even beautiful.

1.1 What Is a Web?

The Web architecture was built by standing on the shoulders of giants. Writing in The Atlantic magazine in 1945 [Bush and Wang 1945], Vannevar Bush identified the problems in managing large collections of documents and the links we make between them. Bush’s proposal was to consider this as a scientific problem, and among the ideas he proposed was the one of externalizing and automating the storage and management of association links we make in our readings. He also illustrated his ideas with an imaginary device he called the Memex (“memory extension”) that would assist us in studying, linking, and remembering the documents we work with and the association links we weave between them. Twenty years later, Ted Nelson quoted As We May Think and proposed using a computer to implement the idea, using hypertext and hypermedia structures to link parts of documents together. In the late sixties, Douglas Engelbart and the Augment project provided the mouse and new means of interaction and applied them in particular to hypertext editing and browsing. The beginning of the seventies brought the work of Vinton Cerf and the emergence of the Internet, which connected computers all around the world.

By the end of the eighties, Tim Berners-Lee was able to stand on the shoulders of these giants when he proposed a new breakthrough: an architecture for distributing hypermedia on the Internet, which we now know as the WWW. The Web provides a hypertext infrastructure that links documents across the Internet, that is, connecting documents that are not on the same machine. And so the Web was born. The Web architecture includes two important parts: Web clients, the most well known being the Web browser, and Web servers, which serve documents and data to the clients whenever they require it. For this architecture to work, there have to be three initial essential components. First, addresses that allow us to identify and locate the document on the Web; second, communication protocols that allow a client to connect to a server, send a request, and get an answer; and third, representation languages to describe the content of the pages, the documents that are to be transferred. These three components comprise a basic Web architecture as described in Jacobs and Walsh [2004], which the Semantic Web standards, which we will describe later in this book, extend in order to publish semantic data on the Web.

The idea of a web of information was once a technical idea accessible only to highly trained, elite information professionals: IT administrators, librarians, information architects, and the like. Since the widespread adoption of the WWW, it is now common to expect just about anyone to be familiar with the idea of a web of information that is shared around the world. Contributions to this web come from every source, and every topic you can think of is covered.

Essential to the notion of the Web is the idea of an open community: anyone can contribute their ideas to the whole, for anyone to see. It is this openness that has resulted in the astonishing comprehensiveness of topics covered by the Web. An information “web” is an organic entity that grows from the interests and energy of the communities that support it. As such, it is a hodgepodge of different analyses, presentations, and summaries of any topic that suits the fancy of anyone with the energy to publish a web page. Even as a hodgepodge, the Web is pretty useful. Anyone with the patience and savvy to dig through it can find support for just about any inquiry that interests them. But the Web often feels like it is a mile wide but an inch deep. How can we build a more integrated, consistent, deep Web experience?

1.2 Communicating with Data

Suppose you are thinking about heading to your favorite local restaurant, Copious, so you ask your automated personal assistant, “What are the hours for Copious?” Your assistant replies that it doesn’t have the hours for Copious. So you go to a web page, look them up, and find right there, next to the address and the daily special, the opening hours. How could the web master at Copious have told your assistant about what was on the web page? Then you wouldn’t just be able to find out the opening hours, but also the daily special.

Suppose you consult a web page, looking for a major national park, and you find a list of hotels that have branches in the vicinity of the park. You don’t find your favorite hotel, Mongotel. But you go to their web site, and find a list of their locations. Some of them are near the park. Why didn’t the park know about that? How could Mongotel have published its locations in a way that the park’s web site could have found them?

Going one step further, you want to figure out which of your hotel locations is nearest to the park. You have the address of the park, and the addresses of your hotel locations. And you have any number of mapping services on the Web. One of them shows the park, and some hotels nearby, but they don’t have all the Mongotel locations. So you spend some time copying and pasting the addresses from the Mongotel page to the map, and you do the same for the park. You think to yourself, “Why should I be the one to copy this information from one page to another? Whose job is it to keep this information up to date?” Of course, Mongotel would be very happy if the data on the mapping page would be up to date. What can they do to make this happen?

Suppose you are maintaining an amateur astronomy resource, and you have a section about our solar system. You organize news and other information about objects in the solar system: stars (well, there’s just one of those), planets, moons, asteroids, and comets. Each object has its own web page, with photos, essential information (mass, albedo, distance from the sun, shape, size, what object it revolves around, period of rotation, period of revolution, etc.), and news about recent findings, observations, and so on. You source your information from various places; the reference data comes from the International Astronomical Union (IAU), and the news comes from a number of feeds.

One day, you read in the newspaper that the IAU has decided that Pluto, which up until 2006 was considered a planet, should be considered a member of a new category called a “dwarf planet”! You will need to update your web pages, since not only has the information on some page changed, but so has the way you organize it; in addition to your pages about planets, moons, asteroids, and so on, you’ll need a new page about “dwarf planets.” But your page about planets takes its information from the IAU already. Is there something they could do, so that your planet page would list the correct eight planets, without you having to re-organize anything?

You have an appointment with your dentist, and you want to look them up. You remember where the office is (somewhere on East Long Street) and the name of the dentist, but you don’t remember the name of the clinic. So you look for dentists on Long Street. None of them ring a bell. When you finally find their web page, you see that they list themselves as “oral surgeons,” not dentists. Whose job is it to know all the ways a dentist might list themselves?

You are a scientist researching a particular medical condition, whose metabolic process is well understood. From this process, you know a number of compounds that play a role in the process. Researchers around the world have published experimental results about organic compounds linked to human metabolism. Have any experiments been done about any of the compounds you are interested in? What did they measure? How can the scientists of the world publish their data so that you can find it?

Tigerbank lends money to homeowners in the form of mortgages, as does Makobank; some of them are at fixed interest rates, and some float according to a published index. A clever financial engineer puts together a deal where one of Tigerbank’s fixed loan payments is traded for one of Makobank’s floating loan payments. These deals make sense for people who want to mitigate the different risk profiles of these loans. Is this sort of swap a good deal or not? We have to compare the terms of Tigerbank’s loan with those of Makobank’s loan. How can the banks describe their loans in terms that participants can use to compare them?

What do these examples have in common? In each case, someone has knowledge of something that they want to share. It might be about their business (hours, daily special, locations, business category), or scientific data (experimental data about compounds, the classification of a planet), or information about complex instruments that they have built (financial instruments). It is in the best interests of the entities with the data to publicize it to a community of possible consumers, and make it available via many channels: the web page itself, but also via search engines, personal assistants, mash-ups, review sites, maps, and so on. But the data is too idiosyncratic, or too detailed, or just too complex to simply publicize by writing a description of it. In fact, it is so much in their interest to get this data out, that they are willing to put some effort into finding the right people who need their data and how they can use it.

Social data

A special case of the desire to share data is social networking. Billions of people share data about their lives on a number of social web sites, including their personal lives as well as their professional lives. It is worth their while to share this data, as it provides ways for them to find new friends, keep in touch with old friends, find business connections, and many other advantages.

Social and professional networking is done in a non-distributed way. Someone who wants to share their professional or personal information signs up for a web service (common ones today include Facebook, LinkedIn, Instagram, and WeChat; others have come and gone, and more will probably appear as time goes on), creates an account that they have control of, and they provide data, in the form of daily updates, photos, tags of places they’ve been and people they’ve been with, projects they have started or completed, jobs they have done, and so on. This data is published for their friends and colleagues, and indeed in some cases for perfect strangers, to search and view.

In these cases, the service they signed up for owns the data, and can use it for various purposes. Most people have experienced the eerie effect of having mentioned something in a social network, only to find a related advertisement appear on their page the following day.

Advertising is a lucrative but mostly harmless use of this data. In 2018, it was discovered that data from Facebook for millions of users had been used to influence a number of high-profile elections around the world, including the US presidential election of 2016 and the so-called “Brexit” referendum in the UK [Meredith 2018]. Many users were surprised that this could happen; they shared their data in a centralized repository over which they had no control.

This example shows the need for a balance of control—yes, I want to share my data in the examples of Section 1.2, and I want to share it with certain people but not with others (as is the case in this section). How can we manage both of these desires? This is a problem of distributed data; I need to keep data to myself if I want to control it, but it has to connect to data around the world to satisfy the reasons why I publish it in the first place.

Learning from data

Data Science has become one of the most productive ways to make business predictions, and is used across many industries, to make predictions for marketing, demand, evaluation of risk, and many other settings in which it is productive to be able to predict how some person will behave or how well some product will perform.

Banking provides some simple examples. A bank is in the business of making loans, sometimes mortgages for homeowners, or automobile loans, small-business loans, and so on. As part of the loan application process, the bank learns a good deal about the borrower. Most banks have been making loans for many decades, and have plenty of data about the eventual disposition of these loans (for example, Were they defaulted? Did they pay off early? Were they refinanced?). By gathering large amounts of this data, machine learning techniques can predict the eventual disposition of a loan based on information gathered at the outset. This, in turn, allows the bank to be more selective in the loans it makes, allowing it to be more effective in its market.

This basic approach has been applied to marketing (identifying more likely sales leads), product development (identifying which features will sell best), customer retention (identifying problems before they become too severe to deal with), medicine (identifying diseases based on patterns in images and blood tests), route planning (finding best routes for airplanes), sports (deciding which players to use at what time), and many other high-profile applications.

In all of these cases, success relied on the availability of meaningful data. In the case of marketing, sales, and manufacturing applications, the data comes from a single source, that is, the sales behavior of the customers of a single company. In the case of sports, the statistical data for the sport has been normalized by sports fans for generations. The data is already aligned into a single representation. This is an important step that allows machine learning algorithms to generalize the data.

The only example in this list where the data is distributed is medicine, where diagnoses come from hospitals and clinics from around the world. This is not an accident; in the case of medicine, disease and treatment codes have been in place for decades to align data from multiple sources.

How can we extend the successful example of machine learning in medicine, to take our machine learning successes from the enterprise level to the industrial level in other industries? We need a way to link together data that is distributed throughout an industry.

1.3 Distributed Data

In the restaurant example, we had data (opening hours, daily special, holiday closings) published so that they can be read by the human eye, but our automated assistant couldn’t read them. One solution would be to develop sophisticated algorithms that can read web pages and figure out the opening hours based on what it sees there. But the restaurant owner knows the hours, and wants prospective patrons to find them, and for them to be accurate. Why should a restaurant owner rely on some third party to facilitate communication to their customers?

A scientific paper that reports on an experimental finding has a very specific audience: other researchers who need to know about that compound and how it reacts in certain circumstances. It behooves both the author and the reader to match these up. Once again, the author does not want to rely on someone else to communicate their value.

This story repeats at every level; a bank has more control over its own instruments if it can communicate their terms in a clear and unambiguous way (to partners, clients, or regulators). The IAU’s charter is to keep the astronomical community informed about developments in observations and classifications. Dentists want their patients to be able to find their clinics.

The unifying theme in all of these examples is a move from a presentation of information for a specific audience, requiring interpretation from a human being, to an exchange of data between machines. Instead of relying on human intuition just in the interpretation of the data, we meet half-way: have data providers make it easier to consume the data. We take advantage of the desire to share data, to make it easier to consume.

Instead of thinking of each data source as a single point that is communicating one thing to one person, it is a multi-use part of an interconnected network of data. Human users and machine applications that want to make use of this data collaborate with data providers, taking advantage of the fact that it is profitable to share your data.

A distributed web of data

The Semantic Web takes this idea one step further, applying it to the Web as a whole. The Web architecture we are familiar with supports a distributed network of hypertext pages that can refer to one another with global links called Uniform Resource Locators (URLs). The Web architecture generalizes this notion to a Uniform Resource Identifier (URI), allowing it to be used in contexts beyond the hypertext Web.

The main idea of the Semantic Web is to support a distributed Web at the level of the data rather than at the level of the presentation. Instead of just having one web page point to another, one data item can point to another, using the same global reference mechanism that the Web uses—URIs. When Mongotel publishes information about its hotels and their locations, or when Copious publishes its opening hour, they don’t just publish a human-readable presentation of this information but instead a distributable, machine-readable description of the data.

The Semantic Web faces the problem of distributed data head-on. Just as the hypertext Web changed how we think about availability of documents, the Semantic Web is a radical way of thinking about data. At first blush, distributed data seems easy: just put databases all over the Web (data on the Web). But in order for this to act as a distributed web of data, we have to understand the dynamics of sharing data among multiple stakeholders across a diverse world. Different sources can agree or disagree, and data can be combined from different sources to gain more insight about a single topic.

Even within a single company, data can be considered as a distributed resource. Multiple databases, from different business units, or from parts of the business that were acquired through merger or corporate buy-out, can be just as disparate as sources from across the Web. Distributed data means that the data comes from multiple stakeholders, and we need to understand how to bring the data together in a meaningful way.

Broadly speaking, data makes a statement that relates one thing to another, in some way. Copious (one thing) opens (a way to relate to something else) at 5:00pm (another thing, this time a value). They serve (another way to relate to something) chicken and waffles (this time, a dish), which itself is made up (another way to relate) of some other things (chicken, waffles, and a few others not in its name, like maple syrup). Any of these things can be represented at any source in a distributed web of data. The data model that the Semantic Web uses to represent this distributed web of data is called the Resource Description Framework (RDF) and is the topic of Chapter 3.

Features of a Semantic Web

The WWW was the result of a radical new way of thinking about sharing information. These ideas seem familiar now, as the Web itself has become pervasive. But this radical new way of thinking has even more profound ramifications when it is applied to a web of data like the Semantic Web. These ramifications have driven many of the design decisions for the Semantic Web standards and have a strong influence on the craft of producing quality Semantic Web applications.

Give me a voice…

On the WWW, publication is by and large in the hands of the content producer. People can build their own web page and say whatever they want on it. A wide range of opinions on any topic can be found; it is up to the reader to come to a conclusion about what to believe. The Web is the ultimate example of the warning caveat emptor (“Let the buyer beware”). This feature of the Web is so instrumental in its character that we give it a name: the AAA Slogan: “Anyone can say Anything about Any topic.”

In a web of hypertext, the AAA slogan means that anyone can write a page saying whatever they please and publish it to the Web infrastructure. In the case of the Semantic Web, it means that our architecture has to allow any individual to express a piece of data about some entity in a way that can be combined with data from other sources. This requirement sets some of the foundation for the design of RDF.

It also means that the Web is like a data wilderness—full of valuable treasure, but overgrown and tangled. Even the valuable data that you can find can take any of a number of forms, adapted to its own part of the wilderness. In contrast to the situation in a large, corporate data center, where one database administrator rules with an iron hand over any addition or modification to the database, the Web has no gatekeeper. Anything and everything can grow there. A distributed web of data is an organic system, with contributions coming from all sources. While this can be maddening for someone trying to make sense of information on the Web, this freedom of expression on the Web is what allowed it to take off as a bottom-up, grassroots phenomenon.

… So I may speak!

In the early days of the hypertext Web, it was common for skeptics, hearing for the first time about the possibilities of a worldwide distributed web full of hyperlinked pages on every topic, to ask, “But who is going to create all that content? Someone has to write those web pages!”

To the surprise of those skeptics, and even of many proponents of the Web, the answer to this question was that everyone would provide the content. Once the Web infrastructure was in place (so that Anyone could say Anything about Any topic), people came out of the woodwork to do just that. Soon every topic under the sun had a web page, either official or unofficial. It turns out that a lot of people had something to say, and they were willing to put some work into saying it. As this trend continued, it resulted in collaborative “crowdsourced” resources like Wikipedia and the Internet Movie Database (IMDb)—collaboratively edited information sources with broad utility. This effect continued as the Web grew to create social networks where a billion people contribute every day, and their contributions come together to become a massive data source with considerable value in its own right.

The hypertext Web grew because of a virtuous cycle that is called the network effect. In a network of contributors like the Web, the infrastructure made it possible for anyone to publish, but what made it desirable for them to do so? At one point in the Web, when Web browsers were a novelty, there was not much incentive to put a page on this new thing called “the Web”; after all, who was going to read it? Why do I want to communicate to them? Just as it isn’t very useful to be the first kid on the block to have a fax machine (whom do you exchange faxes with?), it wasn’t very interesting to be the first kid with a Web server.

But because a few people did have Web servers, and a few more got Web browsers, it became more attractive to have both web pages and Web browsers. Content providers found a larger audience for their work; content consumers found more content to browse. As this trend continued, it became more and more attractive, and more people joined in, on both sides. This is the basis of the network effect: The more people who are playing now, the more attractive it is for new people to start playing. Another feature of the Web that made it and its evolutions possible is the fact that it is auto documented, that is, the documentation for building, using, and contributing to the Web is on the Web itself and when an evolution like the semantic Web comes around, it too can be documented on the Web to support the network effect.

A good deal of the information that populates the Semantic Web started out on the hypertext Web, sometimes in the form of tables, spreadsheets, or databases, and sometimes as organized group efforts like Wikipedia. Who is doing the work of converting this data to RDF for distributed access? In the earliest days of the Semantic Web, there was little incentive to do so, and it was done primarily by vanguards who had an interest in Semantic Web technology itself. As more and more data are available in RDF form, it becomes more useful to write applications that utilize this distributed data. Already there are several large, public data sources available in RDF, including an RDF image of Wikipedia called dbpedia, and a surprisingly large number of government datasets. Small retailers publish information about their offerings using a Semantic Web format called RDFa, using a shared description framework called Schema.org (Section 10.1). Facebook allows content managers to provide structured data using RDFa and a format called the Open Graph Protocol. The presence of these sorts of data sources makes it more useful to produce data in linked form for the Semantic Web. The Semantic Web design allows it to benefit from the same network effect that drove the hypertext Web.

The Linked Open Data Cloud (http://lod-cloud.net/) is an example of an effort that has followed this path. Starting in 2007, a group of researchers at the National University of Ireland began a project to assemble linked datasets on a variety of topics. Figure 1.1 shows the growth of the Linked Open Data Cloud from 2007 until 2017, following the network effect. At first, there was very little incentive to include a dataset into the cloud, but as more datasets were linked together (including Wikipedia), it became easier and more valuable to include new datasets. The Linked Open Data Cloud includes datasets that share some common reference; the web of data itself is of course much larger. The Linked Open Data Cloud includes datasets in a wide variety of fields, including Geography, Government, Life Sciences, Linguistics, Media, and Publication.

Another effort built on these same standards is referred to as knowledge graphs. This name refers to a wide range of information sharing approaches, but the term gained popularity around 2012 when Google announced that it was using something it called a knowledge graph to make searches more intelligent. Google felt that the use of the name Knowledge Graph, instead of something that seemed more esoteric like Semantic Web, would make it easier for people to understand the basic concept. That is rather than a Web of Semantics they would prefer to call it a Graph of Knowledge. The name has caught on, and is now used in industrial settings to refer to the use of Semantic Web technology and approaches in an enterprise setting. The basics of the technology are the same; the standards we outline in this book apply equally well to the Semantic Web, linked data, or knowledge graphs.

What about the round-worlders?

The network effect has already proven to be an effective and empowering way to muster the effort needed to create a massive information network like the WWW; in fact, it is the only method that has actually succeeded in creating such a structure. The AAA slogan enables the network effect that made the rapid growth of the Web possible. But what are some of the ramifications of such an open system? What does the AAA slogan imply for the content of an organically grown web?

Figure 1.1 Number of linked open datasets on the Web in the Linked Open Data Cloud. From the Linked Open Data Cloud at lod-cloud.net.

For the network effect to take hold, we have to be prepared to cope with a wide range of variance in the information on the Web. Sometimes the differences will be minor details in an otherwise agreed-on area; at other times, differences may be essential disagreements that drive political and cultural discourse in our society. This phenomenon is apparent in the hypertext Web today; for just about any topic, it is possible to find web pages that express widely differing opinions about that topic. The ability to disagree, and at various levels, is an essential part of human discourse and a key aspect of the Web that makes it successful. Some people might want to put forth a very odd opinion on any topic; someone might even want to postulate that the world is round, while others insist that it is flat. The infrastructure of the Web must allow both of these (contradictory) opinions to have equal availability and access.

There are a number of ways in which two speakers on the Web may disagree. We will illustrate each of them with the example of the status of Pluto as a planet:

• They may fundamentally disagree on some topic. While the IAU has changed its definition of planet in such a way that Pluto is no longer included, it is not necessarily the case that every astronomy club or even national body agrees with this categorization. Many astrologers, in particular, who have a vested interest in considering Pluto to be a planet, have decided to continue to consider Pluto as a planet. In such cases, different sources will simply disagree.

• Someone might want to intentionally deceive. Someone who markets posters, models, or other works that depict nine planets has a good reason to delay reporting the result from the IAU and even to spreading uncertainty about the state of affairs.

• Someone might simply be mistaken. Web sites are built and maintained by human beings, and thus they are subject to human error. Some web site might erroneously list Pluto as a planet or, indeed, might even erroneously fail to list one of the eight “nondwarf” planets as a planet.

• Some information may be out of date. There are a number of displays around the world of scale models of the solar system, in which the status of the planets is literally carved in stone; these will continue to list Pluto as a planet until such time as there is funding to carve a new description for the ninth object. Web sites are not carved in stone, but it does take effort to update them; not everyone will rush to accomplish this.

While some of the reasons for disagreement might be, well, disagreeable (wouldn’t it be nice if we could stop people from lying?), from a technical perspective, there isn’t any way to tell them apart. The infrastructure of the Web has to be able to cope with the fact that information on the Web will disagree from time to time and that this is not a temporary condition. It is in the very nature of the Web that there be variations and disagreement.

The Semantic Web is often mistaken for an effort to make everyone agree on a single ontology, that is, to make everyone agree on a single set of terms—but that just isn’t the way the Web works. The Semantic Web isn’t about getting everyone to agree, but rather about coping in a world where not everyone will agree and achieving some degree of interoperability anyway. In the data themselves too we may find disagreement; for instance, the numbers of casualties in a conflict may be reported on the Web of data with very different values from the involved parties. There will always be multiple ontologies and diverging statements, just as there will always be multiple web pages on any given topic. One of the features of the Web that has made it so successful, and so different from anything that came before it, is that it, from the start, has always allowed all these multiple viewpoints to coexist.

To each their own

How can the Web architecture support this sort of variation of opinion? That is, how can two people say different things about the same topic? There are two approaches to this issue. First, we have to talk a bit about how one can make any statement at all in a web context.

The IAU can make a statement in plain English about Pluto, such as “Pluto is a dwarf planet,” but such a statement is fraught with all the ambiguities and contextual dependencies inherent in natural language. We think we know what “Pluto” refers to, but how about “dwarf planet”? Is there any possibility that someone might disagree on what a “dwarf planet” is? How can we even discuss such things?

The first requirement for making statements on a global web is to have a global way of identifying the entities we are talking about. We need to be able to refer to “the notion of Pluto as used by the IAU” and “the notion of Pluto as used by the American Federation of Astrologers” if we even want to be able to discuss whether the two organizations are referring to the same thing by these names.

In addition to Pluto, another object was also classified as a “dwarf planet.” This object is sometimes known as UB313 and sometimes known by the name Xena. How can we say that the object known to the IAU as UB313 is the same object that its discoverer Michael Brown calls “Xena”?

One way to do this would be to have a global arbiter of names decide how to refer to the object. Then Brown and the IAU can both refer to that “official” name and say that they use a private “nickname” for it. Of course, the IAU itself is a good candidate for such a body, but the process to name the object took over two years. Coming up with good, agreed-on global names is not always easy business.

On the Web, we name things with URIs. The URI standard provides rules to mint identifiers for anything around us. The most common form of URIs are the URLs commonly called Web addresses (for example, http://www.inria.fr/) that locate a specific resource on the Web. In the absence of an agreement, different Web authors will select different URIs for the same real-world resource. Brown’s Xena is IAU’s UB313. When information from these different sources is brought together in the distributed network of data, the Web infrastructure has no way of knowing that these need to be treated as the same entity. The flip side of this is that we cannot assume that just because two URIs are distinct, they refer to distinct resources. This feature of the Semantic Web is called the Nonunique Naming Assumption; that is, we have to assume (until told otherwise) that some Web resource might be referred to using different names by different people. It’s also crucial to note that there are times when unique names might be nice, but it may be impossible. Some other organization than the IAU, for example, might decide they are unwilling to accept the new nomenclature.

There’s always one more

In a distributed network of information, as a rule we cannot assume at any time that we have seen all the information in the network, or even that we know everything that has been asserted about one single topic. This is evident in the history of Pluto and UB313. For many years, it was sufficient to say that a planet was defined as “any object of a particular size orbiting the sun.” Given the information available during that time, it was easy to say that there were nine planets around the sun. But the new information about UB313 changed that; if a planet is defined to be any body that orbits the sun of a particular size, then UB313 had to be considered a planet, too. Careful speakers in the late twentieth century, of course, spoke of the “known” planets, since they were aware that another planet was not only possible but even suspected (the so-called “Planet X,” which stood in for the unknown but suspected planet for many years).

The same situation holds for the Semantic Web. Not only might new information be discovered at any time (as is the case in solar system astronomy), but, because of the networked nature of the Web, at any one time a particular server that holds some unique information might be unavailable. For this reason, on the Semantic Web, we can rarely conclude things like “there are nine planets,” since we don’t know what new information might come to light.

In general, this aspect of a Web has a subtle but profound impact on how we draw conclusions from the information we have. It forces us to consider the Web as an Open World and to treat it using the Open World Assumption. An Open World in this sense is one in which we must assume at any time that new information could come to light, and we may draw no conclusions that rely on assuming that the information available at any one point is all the information available.

For many applications, the Open World Assumption makes no difference; if we draw a map of all the Mongotel hotels in Boston, we get a map of all the ones we know of at the time. The fact that Mongotel might have more hotels in Boston (or might open a new one) does not invalidate the fact that it has the ones it already lists. In fact, for a great deal of Semantic Web applications, we can ignore the Open World Assumption and simply understand that a semantic application, like any other web page, is simply reporting on the information it was able to access at one time.

The openness of the Web only becomes an issue when we want to draw conclusions based on distributed data. If we want to place Boston in the list of cities that are not served by Mongotel (for example, as part of a market study of new places to target Mongotels), then we cannot assume that just because we haven’t found a Mongotel listing in Boston, no such hotel exists.

As we shall see in the following chapters, the Semantic Web includes features that correspond to all the ways of working with Open Worlds that we have seen in the real world. We can draw conclusions about missing Mongotels if we say that some list is a comprehensive list of all Mongotels. We can have an anonymous “Planet X” stand in for an unknown but anticipated entity. These techniques allow us to cope with the Open World Assumption in the Semantic Web, just as they do in the Open World of human knowledge.

In contrast to the Open World Assumption, most data systems operate under the Closed World Assumption, that is, if we are missing some data in a document or a record, then that data is simply not available. In many situations (such as when evaluating documents that have a set format or records that conform to a particular database schema), the Closed World Assumption is appropriate. The Semantic Web standards have provisions for working with the Closed World Assumption when it is appropriate.

The nonunique name of the Semantic Web

One problem the first time you discover linked data on the Web and Semantic Web is that this evolution of the Web is perceived and presented under different names, each name insisting on a different facet of the overall architecture of this evolution. In the title of this book, we refer to the Semantic Web, emphasizing the importance of meaning to data sharing. The Semantic Web is known by many other names. The name “Web of data” refers to the opportunity now available on the Web to open silos of data of all sizes, from the small dataset of a personal hotel list up to immense astronomic databases, and to exchange, connect, and combine them on the Web according to our needs. The name “linked data” refers to the fact that we can use the Web addressing and linking capabilities to link data pieces inside and between datasets across the Web much in the same way we reference and link Web pages on the hypertext Web. Only this time, because we are dealing with structured data, applications can process these data and follow the links to discover new data in many more automated ways. The name “linked open data” focuses on the opportunity to exploit open data from the Web in our applications and the high benefit there is in using and reusing URIs to join assertions from different sources. This name also reminds us that linked data are not necessarily open and that all the techniques we are introducing here can also be used in private spaces (intranets, intrawebs, extranets, etc.). In an enterprise, we often refer to a “Knowledge Graph,” which is specific to that enterprise, but can include any information that the enterprise needs to track (including information about other enterprises that it does business with). The name “Semantic Web” emphasizes the ability we now have for exchanging our data models, schemas, vocabularies, in addition to datasets, and the associated semantics in order to enrich the range of automatic processing that can be performed on them as we will see in Chapter 7.

1.4 Summary

The aspects of the Web we have outlined here—the AAA slogan, the network effect, nonunique naming, and the Open World Assumption—already hold for the hypertext Web. As a result, the Web today is something of an unruly place, with a wide variety of different sources, organizations, and styles of information. Effective and creative use of search engines is something of a craft; efforts to make order from this include community efforts like social bookmarking and community encyclopedias to automated methods like statistical correlations and fuzzy similarity matches.

For the Semantic Web, which operates at the finer level of individual statements about data, the situation is even wilder. With a human in the loop, contradictions and inconsistencies in the hypertext Web can be dealt with by the process of human observation and application of common sense. With a machine combining information, how do we bring any order to the chaos? How can one have any confidence in the information we merge from multiple sources? If the hypertext Web is unruly, then surely the Semantic Web is a jungle—a rich mass of interconnected information, without any road map, index, or guidance.

How can such a mess become something useful? That is the challenge that faces the working ontologist. Their medium is the distributed web of data; their tools are the Semantic Web languages RDF, RDF Schema (RDFS), SPARQL, Simple Knowledge Organization System (SKOS), Shapes Constraint Language (SHACL), and Web Ontology Language (OWL). Their craft is to make sensible, usable, and durable information resources from this medium. We call that craft modeling, and it is the centerpiece of this book.

The cover of this book shows a system of channels with water coursing through them. If we think of the water as the data on the Web, the channels are the model. If not for the model, the water would not flow in any systematic way; there would simply be a vast, undistinguished expanse of water. Without the water, the channels would have no dynamism; they have no moving parts in and of themselves. Put the two together, and we have a dynamic system. The water flows in an orderly fashion, defined by the structure of the channels. This is the role that a model plays in the Semantic Web.

Without the model, there is an undifferentiated mass of data; there is no way to tell which data can or should interact with other data. The model itself has no significance without data to describe it. Put the two together, however, and you have a dynamic web of information, where data flow from one point to another in a principled, systematic fashion. This is the vision of the Semantic Web—an organized worldwide system where information flows from one place to another in a smooth but orderly way.

Fundamental concepts

The following fundamental concepts were introduced in this chapter.

• The AAA slogan—Anyone can say Anything about Any topic. One of the basic tenets of the Web in general and the Semantic Web in particular.

• Open World/Closed World—A consequence of the AAA slogan is that there could always be something new that someone will say; this means that we must assume that there is always more information that could be known.

• Nonunique naming—Since the speakers on the Web won’t necessarily coordinate their naming efforts, the same entity could be known by more than one name.

• The network effect—The property of a web that makes it grow organically. The value of joining in increases with the number of people who have joined, resulting in a virtuous cycle of participation.

• The data wilderness—The condition of most data on the Web. It contains valuable information, but there is no guarantee that it will be orderly or readily understandable.