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Computer Networks 42 (2003) 551–556
www.elsevier.com/locate/comnet
Guest Editorial
The Semantic Web: an evolution for a revolution
The Web is an indisputable success. It has re-
volutionised the publication and dissemination of
information. However, to access and interpret thatinformation necessitates human intervention. To
discover an expert on post-impressionist art, for
example, is likely to necessitate combining infor-
mation spread across several different Web re-
sources covering art collections, artist biographies
and art history. A professor who wrote a book on
Van Gogh could be inferred to be an expert (as
authorship implies expertise) if it is known thatVan Gogh is a post-impressionist, even if no
mention of this is made on the professor�s Web
site. A published professor would more likely be
considered an expert than a high school student
whose essay on Gauguin (another post-impres-
sionist) is also published as a Web page. To search
for and link information, a person or some appli-
cation must interpret the content of a Web re-source––the person or application must determine
what the content is about. To infer new informa-
tion that is not explicitly asserted requires rea-
soning with knowledge that is embedded in the
application or in the head of the person reading
the Web page. This makes the Web today a place
where humans are doing the processing or the
processing is hard-wired into applications.The vision of the Semantic Web, as proposed by
Tim Berners-Lee et al. [1], is to evolve the currentWeb to one where information and services areunderstandable and usable by computers as well ashumans––to create a ‘‘Web for machines’’. Theautomated processing of Web content requires, atits heart, that explicit machine-processable se-mantics be associated with Web resources as
metadata so that it can be interpreted and com-
1389-1286/03/$ - see front matter � 2003 Elsevier Science B.V. All r
doi:10.1016/S1389-1286(03)00222-6
bined. The Semantic Web does not replace the
Web; it sits on top of the Web as an integrating
descriptive fabric. Such an environment forms aplatform for search engines, information brokers
and ultimately the �intelligent� agents.
1. The components of the Semantic Web
The Semantic Web makes huge demands on
many areas of computing, and in particular re-
quires the confluence of distributed systems, data
and knowledge management, and artificial intelli-
gence. Current efforts concentrate on three main
areas:
• Specifying the languages that will form the fab-
ric of the Semantic Web;
• Specifying and developing the architectural
components and tools forming the infrastruc-
ture of the Semantic Web;
• Prototyping applications using the languages,
the components and defining the content neces-sary.
All of these areas are developing in parallel and
yet are interdependent. Consequently, the devel-
opment has been a maelstrom of research coupled
concurrently with standards activity in W3C, and
early experiments and prototypes running along-
side commercial developments.
1.1. The language fabric
The first steps have been made in defining the
shared languages needed to describe the metadata
ights reserved.
552 Guest Editorial / Computer Networks 42 (2003) 551–556
and the knowledge that will make up the fabric of
the Semantic Web. The DARPA Agent Markup
Language (DAML) initiative [2] and the EU On-
toWeb Thematic Network [3] have been instru-
mental in laying the foundations; these efforts have
been taken up the World Wide Web Consortium(W3C).
The semantic languages are built upon those
already present in the Web (Fig. 1). URIs and
namespaces provide a means of identifying re-
sources. XML provides the common syntax for
machine understandable statements. All the other
languages are encoded in XML. A common mis-
conception is that XML tags provide explicit se-mantics. They do not as the tags are only labels
interpretable by applications or by whatever people
choose them to mean. The Resource Description
Framework (RDF) family of languages [4] provide
a data model for asserting metadata statements for
annotating Web resources without having to have
write access to those resources. In keeping with the
Fig. 1. Language layers
democratic nature of the Web multiple, and po-
tentially conflicting statements, can be made on the
same resource. For example, asserting that Van
Gogh is a post-impressionist painter and that Van
Gogh painted ‘‘The Sunflowers’’.
Shared ontologies supply the vocabulary ofterms used by metadata in order that the appli-
cations and people share a common language and
a common understanding of what the terms mean
(their semantics). Ontologies form shared models
of knowledge. For example, post-expressionism is
a kind of art movement; a painting is a kind of art
artefact; artists paint paintings; a painting can
have a style. Languages for specifying ontologiesinclude RDFSchema [5], DAML+OIL [6], OWL
[7], and Topic Maps [8]. Logical axioms in the
DAML+OIL and OWL languages support the
inference of new metadata and new knowledge
from that explicitly stated; for example, the state-
ment that van Gogh paints a painting that has
a style post-impressionist implies he is a post-
and technologies.
Guest Editorial / Computer Networks 42 (2003) 551–556 553
impressionist artist. Other languages such as
RuleML [9] support other forms of logical
deduction.
Proof and trust models and languages are those
that are the most embryonic. Proof is the provision
of explanation––why was certain knowledge in-ferred. Trust is an attribution of metadata state-
ments––who made those statements. Assertions
about post-impressionism by a professor are more
trusted than those of a high school student.
1.2. Infrastructure
The minimal components include annotationmechanisms, repositories for annotations and on-
tologies with associated query and lifecycle man-
agement, and inference engines that are resilient,
reliable and perform well. Languages for querying
RDF annotations need to be defined and imple-
mented. We need tools to: acquire metadata and
ontologies (manually and automatically); describe
resources with metadata; and for versioning, up-date, security, view management and so on.
Methods for achieving scalability and robustness
need to be developed.
1.3. Applications
The search for ‘‘the killer application’’ for the
Semantic Web is a perennial occupation. However,instead of thinking in terms of killer applications
we should think in terms of killer functionality. The
applications are those that exist already––infor-
mation search, integration, discovery, exchange––
but improved through the provision of semantics.
The association of simple metadata with a Web
resource with simple queries over that metadata
would give a small but not insignificant improve-ment in information integration [10]. More ambi-
tious ideas of the Semantic Web propose an
environment where software agents are able to
dynamically discover, interrogate and interoperate
resources, building and disbanding virtual problem
solving environments, discovering new facts, and
performing sophisticated tasks on behalf of hu-
mans [1]. This revolution will only occur once theevolution of the Web to the Semantic Web reaches
a critical mass, as was the case with the Web itself.
Two application areas that have come to the
fore are: knowledge management and portals, and
Web services.
Web Services are Web-accessible programs and
devices, the latest generation of distributed com-
puting, that will transform the Web from a col-lection of information to a distributed device of
computation. Web services and the Semantic Web
have a symbiotic relationship. The Semantic Web
infrastructure of ontology services, metadata an-
notators, reasoning engines and so on will be de-
livered as Web services. In turn Web services need
semantic-driven descriptions for discovery, nego-
tiation and composition.Knowledge management moves the Web from a
document-oriented view of information to a
knowledge item view. This is the idea of the Web as
a large knowledge base rather than a document
collection. Knowledge portals provide views onto
domain-specific information to enable their users to
find relevant information. Ontologies and knowl-
edge bases form the backbone of such systems.
2. In this issue
In 2002, the 11th International World Wide
Web Conference in Waikiki, Hawaii, USA, had its
first dedicated Semantic Web track. Most of the
papers in this issue are extended versions of papersoriginally published in that track. They range over
the three areas of fabric, infrastructure and ap-
plications.
We start and end with applications. First we
have an example of a pragmatic application from
the knowledge management area. TAP: A Se-
mantic Web Platform by Guha and McCool de-
scribes a Semantic Search application that buildson simple tools that make the Web a giant
distributed database (a ‘‘Data Web’’). Local, in-
dependently-managed knowledge bases are aggre-
gated to form selected centres of knowledge useful
for particular applications, using a set of protocols
and conventions that create a coherent whole of
independently-produced bits of information. The
authors� emphasise the creation of global agree-ments on vocabularies and the need for scalable
and deployable query systems.
554 Guest Editorial / Computer Networks 42 (2003) 551–556
We then have three papers on infrastructure
and tools. To make the Semantic Web we must
lower the barriers of entry. Semantics must emerge
without encumbering users. In CREAM: CREAt-
ing Metadata for the Semantic Web, Handschuh
and Staab present a framework for the creation ofmetadata for existing Web pages and for gathering
metadata while creating content for pages. They
demonstrate the OntoMat ontology-driven anno-
tation tool for web resources. Fillies, Wood-
Albrecht and Weichhardt re-enforce the point that
metadata must be easy for users to create and
should be incidentally captured as a by-product of
their regular activities if it is to be realisticallyobtained. In Pragmatic Applications of the Se-
mantic Web using SemTalk they present a graph-
ical editor to create RDF-like schema and
workflows, and capture knowledge as a natural
by-product of daily work with Microsoft Office
products. The authors show examples from real
applications in a Swiss bank, a German health
insurance company and a German financial insti-tution. The end result of efficient and effective
annotation systems such as CREAM and SemTalk
is that large volumes of RDF descriptions are
appearing and are being stored in repositories. Yet
we have still to have the corresponding efficient
and effective query languages for RDF. In Que-
rying the Semantic Web with RQL, Karvounarakis
et al. propose a query language for RDF, anddemonstrate its effectiveness over an implementa-
tion of an RDF database.
Three papers then take up the Semantic Web
services application theme. Florescu, Gr€uunhagenand Kossmann in XL: An XML Programming
Language for Web Service Specification and Com-
position continue the language theme by presenting
XL, an XML programming language for the im-plementation of Web Services. This work uses the
languages at the ‘‘bottom’’ of the language stack.
Moving up to the use of ontology languages, in
Semantic Web Support for the Business-to-Business
E-Commerce Pre-Contractual Lifecycle, Trastour,
Bartolini and Preist present how the reasoning
capabilities of the Semantic Web language
DAML+OIL can be used to infer matchmakingpossibilities between choices of services, and sup-
port negotiation and service level agreements. Fi-
nally, moving further up the stack, Narayanan and
McIlraith in Analysis and Simulation of Web Ser-
vices use the DAML-S DAML+OIL ontology for
describing the capabilities of Web services, but go
one step further by encoding service descriptions in
a Petri Net formalism and by providing decisionprocedures for Web service simulation, verification
and composition. This shows the different kinds of
inference systems needed by Web Services and the
Semantic Web.
3. The first steps on the road
The evolution of the Web to the revolutionary
possibilities of the Semantic Web has barely
begun. Despite the range of disciplines and tech-
nologies needed to achieve the Semantic Web, the
AI community appears the most engaged. There
are many prospects for AI techniques: Semantic
Web services are recast as variations of agent ne-
gotiation, planning and rule-based systems; ma-chine learning is used for emergent metadata
and ontologies; ontology development, evolution,
and merging draws upon knowledge acquisition
and knowledge representation. However, viewing
the Web as just an application of current AI
technologies that are at hand is a mistake. This is
an interdisciplinary research challenge, encom-
passing distributed computing, databases, digitallibraries, hypermedia, and so on.
The Web was successful because it scaled, by
challenging assumptions on link consistency and
completeness, and because it was simple. To bring
Semantic Web technologies to the Web means
making a similar stand. What are the challenges?
• The Web is vast, so solutions have to scale. Rea-soning engines must perform quickly and ro-
bustly.
• The Web is here––we have a legacy so we will
have a mixed environment where some re-
sources are ‘‘semantic’’ and some are just
‘‘Web’’. We must have a clear and achievable
migration path from non-semantic to semantic.
• The Web is democratic––all are knowledge ac-quisition experts and all are knowledge model-
lers. The barriers of admission must be low
Guest Editorial / Computer Networks 42 (2003) 551–556 555
enough for most users to participate to the de-
gree that is appropriate for them.
• The Web grows from the bottom. Most people
wrote their first HTML by editing a third
partys. The Semantic Web will arise fromfragments of metadata and ontologies being
copied in a similar way. For example, new con-
cepts for ontologies will be produced ‘‘just in
time’’ by annotators; and rather than a few
large, complex, consistent ontologies, shared
by many users, there will be many small onto-
logical components.
• The Web is volatile and changeable––resourcesappear and disappear, resources change. Ontol-
ogies change. What if a piece of metadata is
grounded on a term in an ontology that no
longer exists?
• The Web is dirty––there is no way to ensure
consistency or whether information is trustwor-
thy, and provenance is unknown. However, tol-
erance of error does not necessarily mean oneshould be oblivious to it.
• The Web is heterogeneous––no one solution or
one technology will be adopted; no one ontology
will prevail; no one set of metadata will apply to
a resource. Agreements are difficult, and map-
pings and translations will be commonplace.
To achieve scalability of technologies andmanagement we should recognise that there will
not be a Semantic Web; there will be many se-
mantic webs. High end semantic web applications
will be comparatively few, frequently within in-
tranets, forming islands of quality semantic webs
for specific communities with high quality anno-
tation, large and good quality ontologies and
sound and complete reasoning. These islands willfloat in a sea of low grade and volatile metadata,
with ontologies of variable quality and doubtful
longevity and provenance.
It is not clear yet how current applications will
adapt and what new ones will appear when se-
mantics are prevalent and can be assumed to exist.
Some spin-offs have already borne fruit, for ex-
ample the molecular biology community hasadopted DAML+OIL as an interchange language
for their ontologies independent of its role in the
Semantic Web [11]. Every step on the way to the
full vision is itself a revolution, and the evolution-
ary journey itself is stimulating and worthwhile.
For more information go to http://www. seman-
ticweb.org/.
Acknowledgements
I would like to thank all the authors and the
paper reviewers for their efforts in helping me
produce such a high quality issue. I should like to
thank the reviewers from the WWW2002 confer-
ence for their efforts at the beginning. I would also
like to thank Raphael Volz for his comments onthis editorial and Harry Rudin for his support
throughout the editing process.
References
[1] T. Berners-Lee, J. Hendler, O. Lassila, The Semantic Web,
Scientific American, May 2001.
[2] DARPA Agent Markup Language initiative. Available at
<http://www.daml.org>.
[3] The EU OntoWeb Thematic Network. Available at
<http://www.ontoweb.org>.
[4] The Resource Description Framework. Available at
<http://www.w3.org/RDF/>.
[5] RDF Schema. Available at <http://www.w3.org/TR/rdf-
schema/>.
[6] I. Horrocks, DAML+OIL: a reasonable web ontology
language, in: Proceedings of EDBT 2002, March 2002.
[7] The Web Ontology Language OWL. Available at <http://
www.w3.org/2001/sw/WebOnt/>.
[8] J. Park, S. Hunting, D.C. Engelbart, XML Topic Maps:
Creating and Using Topic Maps for the Web, first ed.,
Addison-Wesley Professional, Reading, MA, 2002.
[9] RuleML, The Rule Markup Initiative. Available at <http://
www.dfki.uni-kl.de/ruleml/>.
[10] B. McBride, Four steps towards the widespread adoption
of a semantic web, in: Proceedings of 1st International
Semantic Web Conference (ISWC2002), June 2002, Lec-
ture Notes in Computer Science, 2342, Springer, Berlin,
2002, pp. 419–422.
[11] Global Open Biological Ontologies. Available at <http://
www.geneontology.org/doc/gobo.html>.
Carole Goble
Department of Computer Science
University of Manchester
Oxford Road
M13 9PL Manchester, UK
E-mail address: carole@cs.man.ac.uk
556 Guest Editorial / Computer Networks 42 (2003) 551–556
Carole Goble is a Professor in the De-partment of Computer Science in theUniversity of Manchester. Her re-search interests are centred on theaccessibility of information, primarilythrough the use of ontologies for therepresentation and classification ofmetadata. She works in many appli-cation areas, and in particular LifeSciences. The Information Manage-ment Group that she co-leads is re-nowned for its work on ontologylanguages (OIL, DAML+OIL, OWL),reasoning systems (FaCT) and their
practical application to real problems. Her work on the appli-cation of ontologies to biology and bioinformatics has been
especially influential. She currently has a leading role in twomajor international initiatives: the Semantic Web and the Grid.She has combined these into the Semantic Grid, co-chairing theSemantic Grid Research Group in the Global Grid Forumstandards organisation and directing a major UK BioGrid re-search pilot, myGrid. She chaired the first Semantic Web trackof the World Wide Web Conference in 2002, on which thisspecial issue is based. She serves on many boards and pro-gramme committees including the OntoWeb Thematic Networkexecutive management board, the international Semantic WebScience Association and the EU/NSF joint ad hoc committeeon Semantic Web Services. She is an Editor-in-Chief of the newElsevier Journal of Web Semantics and is a founder of a startup company, Network Inference, specialising in technologiesfor the Semantic Web.
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