Table Of ContentLecture Notes in Computer Science 3564
CommencedPublicationin1973
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GerhardGoos,JurisHartmanis,andJanvanLeeuwen
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DavidHutchison
LancasterUniversity,UK
TakeoKanade
CarnegieMellonUniversity,Pittsburgh,PA,USA
JosefKittler
UniversityofSurrey,Guildford,UK
JonM.Kleinberg
CornellUniversity,Ithaca,NY,USA
FriedemannMattern
ETHZurich,Switzerland
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StanfordUniversity,CA,USA
MoniNaor
WeizmannInstituteofScience,Rehovot,Israel
OscarNierstrasz
UniversityofBern,Switzerland
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IndianInstituteofTechnology,Madras,India
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UniversityofDortmund,Germany
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MassachusettsInstituteofTechnology,MA,USA
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NewYorkUniversity,NY,USA
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UniversityofCalifornia,Berkeley,CA,USA
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RiceUniversity,Houston,TX,USA
GerhardWeikum
Max-PlanckInstituteofComputerScience,Saarbruecken,Germany
Norbert Eisinger Jan Małuszyn´ski (Eds.)
Reasoning Web
First International Summer School 2005
Msida, Malta, July 25-29, 2005
Tutorial Lectures
1 3
VolumeEditors
NorbertEisinger
Ludwig-Maximilians-UniversitätMünchen,InstitutfürInformatik
Oettingenstr.67,80538München,Germany
E-mail:norbert.eisinger@ifi.lmu.de
JanMałuszyn´ski
LinköpingUniversity,DepartmentofComputerandInformationScience
58183Linköping,Sweden
E-mail:[email protected]
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Preface
This volume contains the tutorial papers of the Summer School “Reasoning
Web,”July25–29,2005(http://reasoningweb.org).TheSchoolwashostedbythe
University of Malta and was organized by the Network of Excellence
REWERSE“ReasoningontheWebwithRulesandSemantics”(http://rewerse.
net), funded by the EU Commission and by the Swiss Federal Office for Educa-
tion and Science within the 6th Framework Programme under the project refer-
encenumber506779.Theobjectiveoftheschoolwastoprovideanintroduction
into methods and issues of the Semantic Web, a major endeavor in current Web
research,wheretheWorldWideWebConsortiumW3Cplaysanimportantrole.
The main idea of the Semantic Web is to enrich Web data with meta-data
carrying a “meaning” of the data and allowing Web-based systems to reason
about data (and meta-data). The meta-data used in Semantic Web applications
is usually linked to a conceptualization of the application domain shared by
different applications. Such a conceptualization is called an ontology and spec-
ifies classes of objects and relations between them. Ontologies are defined by
ontology languages, based on logic and supporting formal reasoning. Justas the
current Web is inherently heterogeneous in data formats and data semantics,
the Semantic Web will be inherently heterogeneous in its reasoning forms. In-
deed, any single form of reasoning turns out to be insufficient in the Semantic
Web. For instance, ontology reasoning in general relies on monotonic negation,
while databases, Web databases, and Web-based information systems call for
non-monotonic reasoning. Constraint reasoning is needed in dealing with time
(for time intervals are to be dealt with). Reasoning with topologies, e.g., in
mobile computing applications, requires planning methods. On the other hand
(forward and/or backward) chaining is the reasoning of choice in coping with
database-likeviews(forviews,i.e.,virtualdata,canbederivedfromactualdata
by operations such as join and projections).
Theprogrammeoftheschoolandtheselectionofthelecturerswasdiscussed
and approved by the REWERSE Steering Committee. This volume contains
10 papers written or co-authored by the lecturers. The papers present some
well-established fundamentals of the Semantic Web and selected research issues
addressed by REWERSE.
The first two papers concern the ontology level of the Semantic Web. The
paper by Grigoris Antoniou, Enrico Franconi, and Frank van Harmelen gives an
introduction to Semantic Web ontology languages and discusses their relation
to description logics. An alternative foundation for Semantic Web reasoning is
F-logic, as discussed in the paper by Michael Kifer.
The next two papers take the perspective of the Web as an information sys-
tem. The first of them, co-authored by James Bailey, Fran¸cois Bry, Tim Furche,
and Sebastian Schaffert, surveys most Web and Semantic Web query languages
VI Preface
so far proposed for the major representation formalisms of the standard and
Semantic Web: XML, RDF and topic maps. The survey stresses the necessity
of an integrated access to the data on the Web that is represented in various
formalisms and discusses the role of reasoning in querying Web data. The size
of this paper is larger than the other ones in this volume. This was necessary in
order to provide a comprehensive and focused survey of numerous Web query
languages.Thesecondpaper,byJos´eJu´lioAlferesandWolfgangMay,addresses
the issue of evolution of Web data and reactivity to events. The paper first dis-
cusses logical foundations of evolution and reactive languages in general and
then focuses on issues specific to evolution and reactivity in the Web and in the
Semantic Web.
User-friendliness of the Web is addressed by the next two papers. The first
of them, by Matteo Baldoni, Cristina Baroglio, and Nicola Henze discusses the
issueofpersonalizationfortheSemanticWeb.Personalizationtechniquesaimat
givingtheuseroptimalsupportinaccessing,retrievingandstoringinformation,
where solutions are built so as to fit the preferences, the characteristics, and the
taste of the individual. The objective of the paper is to provide a coherent in-
troduction into issuesand methods forrealizing personalization in theSemantic
Web. It shows that reasoning is essential for personalization. The paper by Nor-
bert Fuchs, Stefan Ho¨fler, Kaarel Kaljurand, Fabio Rinaldi, and Gerold Schnei-
der gives a systematic introduction into Attempto Controlled English (ACE) a
knowledge representation language readable by human and machine. ACE can
beseenasafirst-orderlogiclanguagewiththesyntaxofanon-ambiguoussubset
of English. It has already been used as an interface language to formal systems,
and due to its ability to express business and policy rules it is of prime interest
for Semantic Web applications.
The remaining papers in this volume show potentially important links be-
tween the Semantic Web and some well-established techniques. The paper by
Gerd Wagner addresses several issues of rule modeling on the basis of the Uni-
fied Modeling Language (UML) proposed by the Object Management Group
(OMG). It discusses similarities and differences between UML class models and
vocabularies of the W3C ontology language OWL. It also shows how UML can
beusedforspecifyingrulesandforprovidingconcisedescriptionsoftheabstract
syntaxofSemanticWeblanguages,suchasRDF,OWL,andemergingSemantic
Web rule languages. The paper by Robert Baumgartner, Thomas Eiter, Georg
Gottlob, Marcus Herzog, and Christoph Koch surveys the state of the art and
techniquesinWebinformationextractionandexplainstheirimportanceforcre-
ation of input data for Semantic Web applications. The paper by Uwe Aßmann
shows that employing ontologies can help to enlarge the software reuse factor
and concludes that ontologies will play an important role in the construction of
software applications, both singular and product lines. This concerns standard
applications as well as Web applications, including Web services. Finally, the
paperbyWl(cid:4)odzimierzDrabentarguesthattypecheckingisneededforWebrule
and query languages. For that purpose the paper presents a formalism for de-
scribing sets of semistructured data. Such sets, to be used as types, are related
Preface VII
toXMLschematadescribedinschemalanguagessuchasDTDorXMLSchema.
Research on their application to typechecking of REWERSE rule languages is
in progress.
July 2005 Norbert Eisinger and Jan Mal(cid:4)uszyn´ski
Co-ordinators of REWERSE Education and Training
Table of Contents
Introduction to Semantic Web Ontology Languages
Grigoris Antoniou, Enrico Franconi, Frank van Harmelen........... 1
Rules and Ontologies in F-Logic
Michael Kifer ................................................. 22
Web and Semantic Web Query Languages: A Survey
James Bailey, Franc¸ois Bry, Tim Furche, Sebastian Schaffert........ 35
Evolution and Reactivity for the Web
Jos´e Ju´lio Alferes, Wolfgang May ................................ 134
Personalization for the Semantic Web
Matteo Baldoni, Cristina Baroglio, Nicola Henze ................... 173
Attempto Controlled English: A Knowledge Representation Language
Readable by Humans and Machines
Norbert E. Fuchs, Stefan H¨ofler, Kaarel Kaljurand, Fabio Rinaldi,
Gerold Schneider .............................................. 213
Rule Modeling and Markup
Gerd Wagner.................................................. 251
Information Extraction for the Semantic Web
Robert Baumgartner, Thomas Eiter, Georg Gottlob, Marcus Herzog,
Christoph Koch ................................................ 275
Reuse in Semantic Applications
Uwe Aßmann ................................................. 290
Towards Types for Web Rule Languages
Wl(cid:3)odzimierz Drabent ........................................... 305
Author Index................................................... 319
Introduction to Semantic Web
Ontology Languages
Grigoris Antoniou1, Enrico Franconi2, and Frank van Harmelen3
1 ICS-FORTH, Greece
[email protected]
2 Faculty of Computer Science, Free University of Bozen–Bolzano, Italy
[email protected]
3 Department of Computer Science, Vrije Universiteti Amsterdam, Netherlands
[email protected]
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
SemanticWeb,andtodiscusstheformalfoundationsoftheselanguages.
Webontologylanguageswillbethemaincarriersoftheinformationthat
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,includingthesemanticsissues.Wethendescribebrieflythemostimpor-
tantontologylanguagesinthedesignoftheSemanticWeb,namelyRDFSchema
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
equippedwithaformalsemantics.Inknowledgerepresentation,anontologyisa
descriptionoftheconceptsandrelationshipsinanapplication 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
N.EisingerandJ.Ma(cid:2)luszyn´ski(Eds.):ReasoningWeb2005,LNCS3564,pp.1–21,2005.
(cid:2)c Springer-VerlagBerlinHeidelberg2005
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-
gieshavebecomeapopularresearchtopic,andseveralAIresearchcommunities,
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,
digitallibraries,e-commerce,andknowledgemanagement.Ontologiesarewidely
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
particularcourses,suchasDiscreteMathematics.Butwealsowanttotalkabout
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.
Animportantuseofclassesistoimposerestrictionsonwhatcanbestated.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
lecturersonly.Thisimposesarestrictiononthevaluesoftheproperty“istaught
by”. In mathematical terms, we restrict the range of the property.
Introduction to Semantic Web Ontology Languages 3
The second statement is non-sensical because only courses can be taught.
This imposes a restriction on the objects to which the property can be applied.
In mathematical terms, we restrict the domain of the property.
Class Hierarchies. Once we have classes we would also like to establish rela-
tionships between them. For example, suppose that we have classes for
– staff members
– academic staff members
– professors
– associate professors
– assistant professors
– administrative staff members
– technical support staff members.
Theseclassesarenotunrelatedtoeachother.Forexample,everyprofessoris
an academic staff member. We say that professor is a subclass of academic staff
member,orequivalently,thatacademicstaffmemberisasuperclass ofprofessor.
The subclass relationship is also called subsumption.
The subclass relationship defines a hierarchy of classes. In general, A is a
subclass of B if every instance of A is also an instance of B.
A hierarchical organisation of classes has a very important practical signifi-
cance, which we outline now. Consider the range restriction
Courses must be taught by academic staff members only.
Suppose Michael Maher was defined as a professor. Then, according to the
restriction above, he is not allowed to teach courses. The reason is that there
is no statement which specifies that Michael Maher is also an academic staff
member.Obviouslyitwouldbehighlycounterintuitivetoovercomethisdifficulty
by adding that statement to our description. Instead we would like Michael
Maher to inherit the ability to teach from the class of academic staff members.
Property Hierarchies. Wesawthathierarchicalrelationshipsbetweenclasses
canbedefined.Thesamecanbedoneforproperties.Forexample,“istaughtby”
isasubpropertyof“involves”.Ifacoursecistaughtbyanacademicstaffmember
a, then c also involves a. The converse is not necessarily true. For example, a
may be the convenor of the course, or a tutor who marks student homework,
but does not teach c.
In general, P is a subproperty of Q if two objects are related by Q whenever
they are related by P.
Summary. Asaconsequenceofthediscussionabove,(Web)ontologylanguages
consist of:
