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.
