Introduction to Semantic Web
Ontology Languages
Grigoris Antoniou1, Enrico Franconi2, and Frank van Harmelen3
1 ICS-FORTH, Greece
antoniou@icsforth.gr
2 Faculty of Computer Science, Free University of Bozen–Bolzano, Italy
franconi@inf.unibz.it
3 Department of Computer Science, Vrije Universiteti Amsterdam, Netherlands
frankh@cs.vu.nl
Abstract. The aim of this chapter is to give a general introduction
to some of the ontology languages that play a prominent role on the
Semantic Web, and to discuss the formal foundations of these languages.
Web ontology languages will be the main carriers of the information that
we will want to share and integrate.
1 Organisation of This Chapter
In section 2 we discuss general issues and requirements for Web ontology lan-
guages, including the semantics issues. We then describe briefly the most impor-
tant ontology languages in the design of the Semantic Web, namely RDF Schema
in section 3 and OWL in section 4. Section 5 contains a brief comparison with
other ontology languages. A brief introduction to description logics and their
relation to the OWL family of web ontology languages is included. The chapter
is concluded by a discussion on the importance of having correct and complete
inference engines for web ontology languages.
2 On Web Ontology Languages
Even though ontologies have a long history in Artificial Intelligence (AI), the
meaning of this concept still generates a lot of controversy in discussions, both
within and outside of AI. We follow the classical AI definition: an ontology is
a formal specification of a conceptualisation, that is, an abstract and simplified
view of the world that we wish to represent, described in a language that is
equipped with a formal semantics. In knowledge representation, an ontology is a
description of the concepts and relationships in an application domain. Depend-
ing on the users of this ontology, such a description must be understandable by
humans and/or by software agents. In many other field – such as in informa-
tion systems and databases, and in software engineering – an ontology would
be called a conceptual schema. An ontology is formal, since its understanding
, LNCS 3564, pp. 1–21, 2005.
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© Springer-Verlag Berlin Heidelberg 2005
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2 G. Antoniou, E. Franconi, and F. van Harmelen
should be non ambiguous, both from the syntactic and the semantic point of
views.
Researchers in AI were the first to develop ontologies with the purpose of fa-
cilitating automated knowledge sharing. Since the beginning of the 90’s, ontolo-
gies have become a popular research topic, and several AI research communities,
including knowledge engineering, knowledge acquisition, natural language pro-
cessing, and knowledge representation, have investigated them. More recently,
the notion of an ontology is becoming widespread in fields such as intelligent
information integration, cooperative information systems, information retrieval,
digital libraries, e-commerce, and knowledge management. Ontologies are widely
regarded as one of the foundational technologies for the Semantic Web: when
annotating web documents with machine-interpretable information concerning
their content, the meaning of the terms used in such an annotation should be
fixed in a (shared) ontology. Research in the Semantic Web has led to the stan-
dardisation of specific web ontology languages.
An ontology language is a mean to specify at an abstract level – that is, at
a conceptual level – what is necessarily true in the domain of interest. More
precisely, we can say that an ontology language should be able to express con-
straints, which declare what should necessarily hold in any possible concrete
instantiation of the domain. In the following, we will introduce various ways
to impose constraints over domains, by means of statements expressed is some
suitable ontology language.
2.1 What Are Ontology Languages
How do we describe a particular domain? Let us consider the domain of courses
and lecturers at Griffith University. First we have to specify the “things” we
want to talk about. Here we will make a first, fundamental distinction. On one
hand we want to talk about particular lecturers, such as David Billington, and
particular courses, such as Discrete Mathematics. But we also want to talk about
courses, first year courses, lecturers, professors etc. What is the difference? In
the first case we talk about individual objects (resources), in the second we talk
about classes (also called concepts) which define types of objects.
A class can be thought of as a set of elements, called the extension of the
class. Individual objects that belong to a class are referred to as instances of
that class.
An important use of classes is to impose restrictions on what can be stated. In
programming languages, typing is used to prevent nonsense from being written
(such as A + 1, where A is an array; we lay down that the arguments of + must
be numbers). The same is needed in RDF. After all, we would like to disallow
statements such as:
– Discrete Mathematics is taught by Concrete Mathematics.
– Room MZH5760 is taught by David Billington.
The first statement is non-sensical because we want courses to be taught by
lecturers only. This imposes a restriction on the values of the property “is taught
by”. In mathematical terms, we restrict the range of the property.