Semantic Web for the Working Ontologist. Dean Allemang
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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.
2 Semantic Modeling
What would you call a world in which any number of people can speak, when you never know who has something useful to say, and when someone new might come along at any time and make a valuable but unexpected contribution? What if just about everyone had the same goal of advancing the collaborative state of knowledge of the group, but there was little agreement (at first, anyway) about how to achieve it?
If your answer is “That sounds like the Web and Semantic Web!”, you are right (and you must have read Chapter 1). If your answer is “It sounds like any large group trying to understand a complex phenomenon,” you are even more right. The jungle that is the Semantic Web is not a new thing; this sort of chaos has existed since people first tried to make sense of the world around them.
What intellectual tools have been successful in helping people sort through this sort of tangle? Any number of analytical tools have been developed over the years, but they all have one thing in common: They help people understand their world by forming an abstract description that hides certain details while illuminating others. These abstractions are called models, and they can take many forms.
How do models help people assemble their knowledge? Models assist in three essential ways:
1. Models help people communicate. A model describes the situation in a particular way that other people can understand.
2. Models explain and make predictions. A model relates primitive phenomena to one another and to more complex phenomena, providing explanations and predictions about the world.
3. Models mediate among multiple viewpoints. No two people agree completely on what they want to know about a phenomenon; models represent their commonalities while allowing them to explore their differences.
The Semantic Web standards have been created not only as a medium in which people can collaborate by sharing information but also as a medium in which people can collaborate on models. Models that they can use to organize the information that they share. Models that they can use to advance the common collection of knowledge.
How can a model help us find our way through the mess that is the Web? How do these three features help? The first feature, human communication, allows people to collaborate on their understanding. If someone else has faced the same challenge that you face today, perhaps you can learn from their experience and apply it to yours. There are a number of examples of this in the Web today, of newsgroups, mailing lists, forums, social media, and wikis where people can ask questions and get answers. In the case in which the information needs are fairly uniform, it is not uncommon for a community or a company to assemble a set of “Frequently Asked Questions,” or FAQs, that gather the appropriate knowledge as answers to these questions. As the number of questions becomes unmanageable, it is not uncommon to group them by topic, by task, by affected subsystem, and so forth. This sort of activity, by which information is organized for the purpose of sharing, is the simplest and most common kind of modeling, with the sole aim of helping a group of people collaborate in their effort to sort through a complex set of knowledge.
The second feature, explanation and prediction, helps individuals make their own judgments based on information they receive. FAQs are useful when there is a single authority that can give clear answers to a question, as is the case for technical assistance for using some appliance or service. But in more interpretative situations, someone might want or need to draw a conclusion for themselves. In such a situation, a simple answer as given in an FAQ is not sufficient. Politics is a common example from everyday life. Politicians in debate do not tell people how to vote, but they try to convince them to vote in one way or another. Part of that convincing is done by explaining their position and allowing the individual to evaluate whether that explanation holds true to their own beliefs about the world. They also typically make predictions: If we follow this course of action, then a particular outcome will follow. Of course, a lot more goes into political persuasion than the argument, but explanation and prediction are key elements of a persuasive argument.