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In the remainder of this chapter we first describe the role of ontologies in
the Semantic Web and then proceed by discussing the languages of the layer
cake.
3.1 Ontologies and the Semantic Web
A key feature of ontologies is that, through formal, real-world semantics and
consensual terminologies, they interweave human and machine understanding
[38]. This important property of ontologies facilitates the sharing and reuse
of ontologies among humans, as well as among machines.
A major reason for the recent increasing interest in ontologies is the devel-
opment of the Semantic Web [13], which can be seen as knowledge manage-
ment on a global scale. Tim Berners-Lee, inventor of the current World Wide
Web and director of the World Wide Web Consortium (W3C), envisions the
Semantic Web as the next generation of the current Web. This “next gener-
ation” will expand upon the prowess of the current Web by adding machine-
readable information and automated services. According to [38], “The explicit
representation of the semantics underlying data, programs, pages, and other
Web resources will enable a knowledge-based Web that provides a qualita-
tively new level of service.” Ontologies provide such an explicit representation
of semantics. The combination of ontologies with the Web has the potential
to overcome many of the problems in knowledge sharing and reuse and in
information integration.
Ontologies interweave human and computer understanding of symbols.
These symbols, also called terms and relations, can be interpreted by both
humans and machines. The meaning for a human is represented by the term
itself, which is usually a word in natural language, and by the semantic re-
lationships between terms. An example of such a human-understandable re-
lationship is a superconcept - subconcept relationship (often referred to by
the term “is-a”). Such a relationship denotes the fact that one concept (the
superconcept) is more general than another (the subconcept). For instance,
the concept Person is more general than Student. Figure 3.2 shows an example
“is-a” hierarchy (or taxonomy), where the more general concepts are located
above the more specialized concepts.
Concepts describe a set of objects in the real world. For example, the
concept PhD-Student aims to capture all existing PhD students. One such
PhD student is Mary, who is modeled in Fig. 3.2 as a box, and has an instance-
of relation to the concept PhD-Student.Thisinstance-of relationship means
that the actual object is captured by the concept PhD-Student. And because
of the formal is-a relationships between the concepts PhD-Student, Researcher,
Student,andPerson, John must also be an instance of the concepts Researcher,
Student,andPerson.
These relationships are fairly easy to understand for the human reader
and, because the meanings of the relationships are formally defined, a machine
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