\chapter{Semantic Web projects}
\label{c5:cap5}

%PROGETTI INTERNAZIONALI
%\section{International projects}
%\label{sec:International}

All around the world several universities, organizations, research
communities started projects related to \emph{the Semantic Web}.
In this chapter we will present some projects and languages of
particular interest for this thesis. To realize this overview we
have chosen the most important works or the most cited papers in
literature and we have tried to understand how the dynamics
problem is faced and which are the most valuable proposals to
solve it. This presentation is not complete and exhaustive; we
have studied these projects in order to evaluate our approach to
dynamics. Further information can be found following the links
present in this chapter.\\

\noindent The \textbf{W3C consortium} has a particular group of
people that are studying  new languages (\textbf{OWL} \emph{Web
Ontology Language} for example) and new standards related to the
new tools that begin to appear on the web. The European Union by
means of the IST (Information Society Technologies) Programme
supports different projects involved in the same area of interest;
we will give information about \textbf{OntoWeb} and
\mbox{\textbf{On-To-Knowledge}} that have done many studies and
involve many researchers from academic and industrial area. Also
some important universities are involved in the study of the
problem. The Karlsruhe University is working on a framework for
the management of the dynamics of ontologies (\textbf{MAFRA} -
\textbf{Kaon}), which is very interesting for its completeness.
Finally we will present \textbf{SHOE}, a language for the Semantic
Web proposed by the J. Heflin and J. Hendler of the University of
Maryland.

%
% Sistemare questa introduzione
%

% Presi dal sito del w3c
\section{W3C Consortium} \label{W3C} The \textbf{World Wide Web
Consortium}\footnote{http://www.w3c.org/} was created in October
1994 to lead the World Wide Web to its full potential by
developing common protocols that promote its evolution and ensure
its interoperability. W3C has around 500 Member organizations from
all over the world and has earned international recognition for
its contributions to the growth of the Web.

W3C Mission is to promote interoperability and to encourage an
open forum for discussion, W3C commits to leading the technical
evolution of the Web. W3C's long term goals for the Web are: 1.
\emph{Universal Access}, to make the Web accessible to all by
promoting technologies that take into account the vast differences
in culture, languages, education, ability, material resources, and
physical limitations of users on all continents; 2. \emph{Semantic
Web}, to develop a software environment that permits each user to
make the best use of the resources available on the Web; 3.
\emph{Web of Trust}, to guide the Web's development with careful
consideration for the new legal, commercial, and social issues
raised by this technology. \\W3C concentrates its efforts on three
principle tasks:
\begin{itemize}
    \item \emph{Vision}: W3C promotes and develops its vision of
    the future of the World Wide Web. Contributions from several
    researchers and engineers enable W3C to identify the technical
    requirements that must be satisfied if the Web is to be a
    truly universal information space.
    \item \emph{Design}: W3C designs Web technologies to realize
    this vision, taking into account existing technologies as well
    as those of the future.
    \item \emph{Standardization}: W3C contributes to efforts to
    standardize Web technologies by producing specifications (called
    ``Recommendations'') that describe the building blocks of the Web.
    W3C makes these Recommendations (and other technical reports)
    freely available to all.
\end{itemize}
W3C Activities are generally organized into groups: \emph{Working
Groups} (for technical developments), \emph{Interest Groups} (for
more general work), and \emph{Coordination Groups} (for
communication among related groups). These groups produce:
technical reports, open source software, and services (e.g.,
validation services). These groups also ensure coordination with
other standards bodies and technical communities. There are
currently over thirty W3C Working Groups.\\

\emph{One of them works in the Semantic Web
area}\footnote{http://www.w3c.org/sw/}. They rely their activities
on the considerations that facilities to put
machine-understandable data on the Web are becoming a high
priority for many communities. The Web can reach its full
potential only if it becomes a place where data can be shared and
processed by automated tools as well as by people. For the Web to
scale, tomorrow's programs must be able to share and process data
even when these programs have been designed totally independently.
\emph{\textbf{The Semantic Web}}, they state, \emph{\textbf{is a
vision}}: \emph{the idea of having data on the web defined and
linked in a way that they can be used by machines not just for
display purposes, but for automation, integration and reuse of
data across various
applications}.\\
%
% Parte presa da del43.pdf di OTK
%

\subsection{OWL Web Ontology Language} \label{c5:OWL}
In November 2001, the W3C started a \textbf{Web Ontology Working
Group} to define a language. This group is in charge of taking to
take DAML+OIL as its starting point and is developing a language
called \textbf{OWL} \emph{\textbf{Web Ontology Language}}. The OWL
is intended to provide a language that can be used to describe the
classes and relations between them that are inherent in Web
documents and applications. The OWL is being designed in order to
provide a language that can be used for applications that need to
understand the content of information instead of just
understanding the human-readable presentation of content. OWL
facilitates more machine readability of web content than XML, RDF,
and RDF-S support by providing an additional vocabulary for term
descriptions. The OWL language is a revision of the DAML+OIL web
ontology language incorporating learnings from the design and
application use of DAML+OIL. \emph{OWL can be viewed as an
extension of a restricted view of the RDF language. This implies
that every OWL document is an RDF document, but not all RDF
documents are OWL documents.}\\

OWL is a language for defining \emph{Web ontologies} and their
associated knowledge bases. In OWL, an \emph{ontology} is a set of
definitions of classes, properties, and constraints on the way
those classes and properties can be employed. \emph{An OWL
ontology may include the following elements}:
\begin{itemize}
    \item taxonomic relations between \emph{classes};
    \item \emph{datatype properties}, descriptions of attributes
    of elements of classes;
    \item object properties, descriptions of relations between elements
    of classes;
\end{itemize}
and, to a inferior degree:
\begin{itemize}
    \item \emph{instances} of classes;
    \item \emph{instances} of properties.
\end{itemize}
Datatype properties and object properties are collectively the
\emph{properties} of a class.

A set of OWL assertions loaded into a reasoning system is called a
knowledge base (KB). These assertions may include facts about
individuals that are members of classes, as well as various
derived facts, facts not literally present in the original textual
representation of the ontology, but entailed (logically implied)
by the semantics of OWL. These assertions may be based on a single
ontology or multiple distributed ontologies that have been
combined using OWL mechanisms.

OWL is a set of three, increasingly complex languages.
\begin{description}
    \item[OWL Lite] has been defined with the intention of creating a simple
language that will satisfy users primarily needing a
classification hierarchy and simple constraint features. For
example, while it supports cardinality constraints, it only
permits cardinality values of 0 or 1.
    \item[OWL DL] includes the complete OWL vocabulary, interpreted under a
number of simple constraints. Primary among these is type
separation. Class identifiers cannot simultaneously be properties
or individuals. Similarly, properties cannot be individuals. OWL
DL is so named due to its correspondence with \emph{description
logics}.
    \item[OWL Full] includes the complete OWL vocabulary, interpreted more
broadly than in OWL DL, with the freedom provided by RDF. In OWL
Full a class can be treated simultaneously as a collection of
individuals (the class \emph{extension}) and as an individual in
its own right (the class \emph{intension}). Another significant
difference from OWL DL is that a \texttt{DatatypeProperty} can be
marked as an \texttt{InverseFunctionalProperty}.
\end{description}
An OWL ontology is made up of several components, some of which
are optional, and some of which may be repeated. Now a simple
example of an OWL ontology is presented.
\begin{verbatim}
<rdf:RDF
    xmlns     ="http://www.example.org/wine#"
    xmlns:vin ="http://www.example.org/wine#"
    xmlns:food="http://www.example.org/food#"
    xmlns:owl ="http://www.w3.org/2002/07/owl#"
    xmlns:rdf ="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
    xmlns:xsd ="http://www.w3.org/2000/10/XMLSchema#"
    xmlns:dte ="http://www.example.org/wine-dt#">
\end{verbatim}
A typical OWL ontology begins with a \emph{namespace declaration}.
The first two declarations identify the namespace associated with
this ontology. The first makes it the default namespace, stating
that unprefixed elements and empty URI references refer to the
current ontology. To state the beginnings of the ontology we use
\begin{verbatim}
<owl:Ontology rdf:about="http://www.example.org/wine">
  <rdfs:comment>An example OWL ontology</rdfs:comment>
  <owl:versionInfo>
      $Id: Overview.html,v 1.2 2002/11/08 16:42:25 connolly Exp $
  </owl:versionInfo>
  <owl:imports rdf:resource=
  "http://www.w3.org/TR/2002/WD-owl-guide-20021104/food.owl"/>
\end{verbatim}
A simple class \texttt{Wine} sub-class of \texttt{PotableLiquid}.
The \texttt{rdfs:label} entry provides an optional human readable
name for this class.
\begin{verbatim}
<owl:Class rdf:ID="Wine">
  <rdfs:subClassOf rdf:resource="#PotableLiquid"/>
  <rdfs:label xml:lang="en">wine</rdfs:label>
  <rdfs:label xml:lang="fr">vin</rdfs:label>
  ...
</owl:Class>
\end{verbatim}
In addition to classes there are also their members. We normally
think of these as \emph{individuals} in our universe of things.
\begin{verbatim}
    <Region rdf:ID="CentralCoastRegion" />
\end{verbatim}
Some words are needed to better explain the difference
between class and individual. A \emph{\textbf{class}} is simply a
name and collection of properties that describe a set of
individuals, and \emph{\textbf{individuals}} are the members of
those sets. Thus classes should correspond to naturally occurring
sets of things in a domain of discourse, and individuals should
correspond to actual entities that can be grouped into these
classes.

\emph{Properties} let us assert general facts about the members of
classes and specific facts about individuals. A property is a
binary relation. Two types of properties are distinguished:
\emph{datatype properties}, relations between elements of classes
and XML datatypes; \emph{object properties}, relations between
elements of two classes. When we define a property there are a
number of ways to restrict the elements of the relation. The
domain and range can be specified. The property can be defined to
be a specialization (subproperty) of an existing property.
\begin{verbatim}
<owl:ObjectProperty rdf:ID="madeFromGrape">
  <rdfs:domain rdf:resource="#Wine"/>
  <rdfs:range rdf:resource="#WineGrape"/>
</owl:ObjectProperty>
\end{verbatim}

The ontology header definition is closed with the following tag.
\begin{verbatim}
  </owl:Ontology>
\end{verbatim}
This prelude is followed by the actual definitions that make up
the ontology and is ultimately closed by
\begin{verbatim}
</rdf:RDF>
\end{verbatim}

Many other elements can be introduced to enrich the ontology. You
can define complex properties (symmetric, transitive, inverseOf,
functional, inverseFunctional), restrictions, cardinality,
ontology mapping, but the description of them is out of the
objective of this thesis. For further information it is possible
to visit the W3C web site. It's important to note that the
language is steadily developing and changes are frequent.\\
Here you can find a guide to OWL:
\texttt{http://www.w3.org/TR/owl-guide/} and here a technical
reference: \texttt{http://www.w3.org/TR/owl-ref/}


%
% parte presa da del43.pdf
%
%A common finding of all the case-studies was that these
%lightweight ontologies went quite a long way towards providing the
%needs of the applications that were built in these casestudies.
%They worked very well as the basis for search, personalization and
%navigation. This finding (the usefulness of lightweight
%ontologies) has had a direct impact on the design of the W3C Web
%Ontology Language OWL (which is directly based on OIL): the
%current draft of this language (June'02) includes a sub language
%OWL Light. This language roughly consists of RDF Schema plus
%equality plus 0/1-cardinality, and is particularly suited to
%express light-weight ontologies of the kind that have been driving
%our case studies.

\section{OntoWeb} \label{ontoweb}
\textbf{OntoWeb}\footnote{http://www.ontoweb.org/}: Ontology-based
information exchange for Knowledge Management and Electronic
Commerce. \textbf{OntoWeb} is a \emph{Thematic Network} founded by
the European Commission. It started in June 2001 and it will end
in May 2004. Its contract number is \mbox{IST-2000-29243}. The
Network is coordinated by The Free University of Amsterdam
(Netherlands) and more than one hundred partners from academic and
industrial area collaborate to the research. The University of
Modena and Reggio E. is a member too.\\

As we have seen in the previous section the Web is evolving
towards a new generation based on the semantics. To do this, we
need ontologies that provide a shared and common domain theories.
They can be seen as meta data that explicitly represent semantics
of data in machine-processable way. Ontology-based reasoning
services can operationalize this semantics to provide various
services. Ontologies help people and computers to have access to
the information they need, and effectively communicate with each
other. The goal of the \mbox{OntoWeb} Network is to bring
researchers and industry together to promote interdisciplinary
work on the Semantic Web.

Another important aspect the project relies on is the
competitiveness of companies active in areas. The business world
has a high rate of change so it is of crucial importance how
effectively the companies acquire, maintain, exchange and have
access to their knowledge, and whether they can deliver the right
information to the right individual or business at the right time
wherever they are. Due to the impact of the Internet, many
organizations are more and more geographically dispersed and
organized around virtual teams. Such organizations need knowledge
management and organizational memory tools that encourage users to
understand each other's changing contextual knowledge and
encourage collaboration while capturing, representing and
interpreting the knowledge resources of their organizations. At
the same time, competitiveness of companies will also depend on
the products and services they offer. The growth of a wide range
of e-commerce services, both to individuals and between
businesses, is contributing to the increase in international
trading of products and services. The ability to find, interrogate
and exchange knowledge is fundamental for B2B and B2C e-Commerce.
\mbox{OntoWeb} will identify and publicize the ontolgy-based
technologies required for the promotion of knowledge management
and e-Commerce.

The \mbox{\textbf{OntoWeb}} \textbf{Network} \emph{will be the
European focal point to bring together activities in the area of
ontology-based methods and tools for the Semantic Web}. It wants
to help companies in the emerging business field by:
\begin{itemize}
    \item Visioning and outlining the current state-of-the-art of the
ontology-related fields in Europe and World Wide and providing a
strategic guide to industrial and commercial applications.
    \item Cooperating with several standardization bodies to promote the
development of Ontology-based Content Standardization and Ontology
Language Standardization.
    \item Providing non-discriminatory access to
services by individuals and businesses and helping in finding and
extracting information from an exponentially growing network.
    \item Organizing dissemination workshops, special interesting groups, a
scientific journal, and training or education courses with special
emphasis on Web based applications, e-commerce, knowledge
management and information integration. It addresses aspects of
know-how transfer between academia and industry about these
emerging ontology-based technologies in order to gain competitive
advantage in these rapidly developing markets.
\end{itemize}
The project's members are divided into \textbf{Special Interest
Groups} (\textbf{SIGs}). They study a particular aspect of the
project and produce deliverables to explain their outcomes. The
OntoWeb SIGs are the following:
\begin{itemize}
    \item \emph{\textbf{Content Standards}} (\textbf{SIG1}). It is
    coordinated by Nicola Guarino (\mbox{LADSEB-CNR}, Italy).
    This group cooperates with current initiatives related to
    ontology-based content standardization,
    develops a framework for characterizing and comparing content
    standardization efforts, pushes the current standardization
    initiatives towards well-defined ontology-based harmonization goals,
    promotes the research on foundational aspects of ontology development
    and stimulates the transfer of research on ontology development from
    academia to industry.
    \item \emph{\textbf{Ontology Language Standards}} (\textbf{SIG2}). It is
    coordinated by Ian Horrocks (University of Manchester, UK) and
    Frank van Harmelen (Vrije Universiteit Amsterdam). The mission of this
    group is to cooperate with related initiatives (DAML) and
    standardization efforts and working groups (W3C ontology language
    standardisation processes). To disseminate the results and transfer
    the research and needs between academia and industry. To provide a
    forum for cooperation in the development of language extensions
    and the initiation of further standardization efforts.
    \item \emph{\textbf{Enterprise-Standard Ontology Environments}} (\textbf{SIG3}).
    The responsible persons for this SIG are Asunci\'on G\'omez-P\'erez
    (University of Marid, Spain) and Mike Brown (SemanticEdge, Germany).
    They try to support a dialog for the development of Quality Requirements
    for enterprise-standard ontology environments. They promote the
    effective application of ontology technology in modern
    enterprises' information technology systems and provide an open
    forum to promote synergy and cooperation amongst the ontology
    technology providers and industrial ontology users.
    \item \emph{\textbf{Industrial Applications}} (\textbf{SIG4}). It is guided by
    Hans Akkermans (Vrije Universiteit Amsterdam, the
    Netherlands). They provide an open forum to bridge the potential
    ontology-related researches with the current electronic commerce
    market, for instance, B2C, B2B.
    \item \textbf{\emph{Language Technology in Ontology Development and Use}}
    (\textbf{SIG5}). Paul Buitelaar, Thierry Declerck and Gunter Neumann
    (DFKI-Language Technology, Germany) are responsible for this
    groups. Their main objective is to define an information portal
    on language technology in ontology development and use (as part
    of the OntoWeb portal). The portal will serve as a platform for
    cooperation between R\&D groups in language technology and
    knowledge management, in order to establish a common understanding
    of market and research needs, of relevant emerging standards, of
    tools and resources and related benchmarking and evaluation efforts,
    and to identify priorities in NLP (Natural Language Processing)
    development for ontology learning and application. Finally, the portal
    will function also as a contact point for related user and technology
    communities, among which in particular the "semantic technology" industry.
\end{itemize}
Further information about the thematic network activities can be
found on the web portal where there are the published
deliverables:\\
URL: http://ontoweb.aifb.uni-karlsruhe.de/About/Deliverables\\

During this thesis we exploit in particular Deliverable D1.3 that
presents a complete survey on ontologies tools (see sec.
\ref{s3:tools}). An interesting area of the portal is called
``\emph{browse ontology}'' where you can navigate the community's
ontology and search documents or information inserting preferences
into the search engine. Moreover a web portal provided by the
University of Madrid allows the user to find a lot of information
and links of other interesting projects, tools, ontology and applications.\\
URL http://babage.dia.fi.upm.es/ontoweb/wp1/OntoRoadMap/index.html\\

Finally we can say that this group is very active and their web
portal is an important and updated reference point where a lot of
information and links about the Semantic Web and Ontologies can be
easily found.


\section{On-To-Knowledge} \label{OTK}
\textbf{On-To-Knowldege}\footnote{http://www.ontoknowledge.org/}
is the name of an European project that aims at creating
innovative tools for knowledge management. It is funded by the
European Commission and is comprised within the EU Information
Society Technologies Programme (EU-IST-10132). It started in
January 2000 and terminated in June 2002. The project's partners
were: Free University of Amsterdam (NL), coordinator, British
Telecom (UK), Swiss Life (CH), AIdministrator (NL), CognIT (NO),
EnerSearch (SE), AIFB University of Karlsruhe (D), OntoText Lab.
(BU).

Information Technology (IT), and especially Internet/WWW, have
boosted the potential for knowledge acquisition and sharing but
information resources are heterogeneous, distributed,
semi-structured, and enormous in size. So is the need for tools
of selective semantic (meaning-oriented) access.\\
\mbox{\textbf{On-To-Knowledge}} \emph{wants to improve access to
information sources and to lower costs to provide and maintaining
large bodies of textual and semistructured information}. In order
to reach that objectives they prepared tools for ontology
construction and interchange, they built up infrastructure for
information representation and they refined the means for
information access and extraction. During the project they
analyzed three large case studies in order to develop the
technology according to the actual needs of large and/or virtual
organizations.

The project is now over and in the last
deliverable\footnote{On-To-Knowledge Final Report} (available on
the web site) they describe their achievements.
\textbf{On-To-Knowledge} provides several breakthroughs on the way
to the next generation of the web: advanced tools, an ontology
language standard for the web, case studies, and knowledge
management methodology. \\Now we summarize some of the principal
results. Figure \ref{f:OTKArchitecture} shows the
\mbox{On-To-Knowledge} architecture.
\begin{figure}[h]
\begin{center}
  \epsfig{figure=OTKArchitecture.eps,height=10cm,angle=0}
  \caption{On-To-Knowledge architecture}\label{f:OTKArchitecture}
\end{center}
\end{figure}\\

\noindent \textbf{Tools.}\\

\noindent A major objective of the project was to create
intelligent software to support users in both having access to
information and in the maintenance, conversion, and acquisition of
information sources. These tools are based on a three{-}layer
architecture around information access, information storage, and
information
generation.\\

\noindent \textbf{\emph{QuizRDF}} is an ontology-based tool for
knowledge discovery which combines traditional keyword querying of
WWW resources with the ability to browse and query against RDF
annotations of those resources. RDF(S) and RDF are used to specify
and populate an ontology and the resulting RDF annotations are
then indexed along with the full text of the annotated resources.
The resulting index permits both keyword querying against the full
text of the document and against the literal values occurring in
the RDF annotations, along with the ability to browse and query
the ontology. This ability to combine searching and browsing
behaviors supports a typical information-seeking task more fully
than ``traditional'' search engine technology.\\
At the link below you can find the starting web page from where
you can perform searches. The tool requires only a web browser and
a network connection to have access to QuizRDF server. After
entering a textual query, the users are presented with a set of
concepts from which they can select in order to significantly
narrow down their search results to a set that is relevant to
them. It can either be used in conjunction with an RDF repository
such as \emph{Sesame} or it can be used in it is own right to
extract RDF from source pages. QuizRDF is a fully developed
prototype and has been used in \mbox{On-To-Knowledge} case
studies. It has been realized by \mbox{BT Exact
Technologies}\footnote{http://www.btexact.com/}.
\\A detailed paper is ``J. Davies, U. Krohn, and R. Weeks,
\emph{QuizRDF: search technology for thesemantic web}. In WWW2002
workshop on RDF \& Semantic Web Applications, 11th International
WWW Conference WWW2002, Hawaii, USA, 2002''.\\

\noindent URL: http://i97.labs.bt.com/quizrdf-bin/rdfsearch/pmika2.89\\


\noindent \textbf{\emph{OntoShare}} enables the storage of the
best practice information in an ontology and the automatic
dissemination of the new best practice information to relevant
co-workers. It also allows users to browse or search the ontology
in order to find the most relevant information to the problem that
they are dealing with at any given time. The ontology helps to
orientate new users and acts as a store for key learning and best
practices accumulated through experience.\\
In other words, OntoShare is a knowledge sharing system that
allows a community to share information. Users can enter
information from the Web or Intranet pages as well as plain text
articles. The system then notifies other users that might be
interested (based upon user profiles) that some data has been
added. OntoShare is a Java application and users only need a web
browser to use it. OntoShare has been realized by \mbox{BT
Exact Technologies}. The starting page is at the link below.\\
A detailed paper is ``J. Davies, A. Duke, and A. Stonkus,
\emph{OntoShare: Using Ontologies for Knowledge Sharing}. In
Proceedings of the WWW2002 Semantic Web workshop, 11th
International WWW Conference WWW2002, Hawaii, USA, 2002''.\\

\noindent URL: http://i97.labs.bt.com/ontoshare/ologin\\


\noindent \textbf{\emph{Spectacle}} organizes the presentation of
information. This presentation is ontology driven. Ontological
information, such as classes or specific attributes of
information, is used to generate exploration contexts for users.
An exploration context makes it easier for users to explore a
domain. The context is related to certain tasks, such as finding
information or buying products.\\
At present we have found on the site of
AIdministrator\footnote{http://www.aidministrator.nl/}, the
developer of Spectacle, three specialized applications:
\emph{Spectacle e-commerce}, \emph{Spectacle analysis},
\emph{Spectacle enterprise}. \\\emph{Spectacle e-commerce} is a
development platform for the creation of web site applications
that shows the products of a virtual store helping visitors to
find products they really want by sharing knowledge about the
products in the store. Spectacle makes it possible for visitors to
explore a virtual store in order to learn what is there. Following
the link below there is a web site demo.\\
\emph{Spectacle enterprise} is dedicated to companies with
valuable information repositories that want to keep track of the
latest information by automatically classifying new information.
This enables workers to find and share information. The process of
adding new information to the system is simple with the help of
automatic classification software. In this way different users have the
information they need presented in the most useful manner, giving them the
possibility to save time in finding information. \\
\emph{Spectacle analysis} provides the qualitative and
quantitative graphical analysis of information, in particular it
helps information professionals understand their information
collections. Spectacle analysis visually shows the cohesion within
large collections of information, typically collections of
documents. It integrates structured and unstructured information
from document repositories, web sites, databases, and other
applications. Figure \ref{f:clustermap} shows the output of the
Spectacle Cluster Map Viewer.\\
\begin{figure}[ht]
  \begin{center}
  \epsfig{figure=clustermap.eps,height=8cm,angle=0}
  \caption{Spectacle Cluster Map Viewer.}\label{f:clustermap}
  \end{center}
  \end{figure}
A detailed paper is ``\emph{Spectacle}, Ch. Fluit, H. ter Horst,
J. van der Meer, M. Sabou,P. Mika, Ch. 9 (pgs. 145-160) of
\emph{Towards the Semantic Web:Ontology-driven Knowledge
Management}, J. Davies, D. Fensel, F. vanHarmelen (eds.), John
Wiley, 2002'' or Deliverable D13 of
On-To-Knowledge\footnote{http://www.ontoknowledge.org/downl/del13.pdf}.\\

\noindent URL: http://www.aidministrator.nl/spectacle/channel\\
/summer2002?section=products/spectacle\\

\noindent \textbf{\emph{OntoEdit}} makes it possible to inspect,
browse, codify and modify ontologies, and thus serves to support
the ontology development and maintenance task. Modelling
ontologies using OntoEdit involves modelling at a conceptual
level, that is: (i) as independently of a concrete representation
language as possible, and (ii) using GUI's representing views on
conceptual structures (concepts, concept hierarchy, relations,
axioms) rather than codifying conceptual structures in ASCII.\\
This tool has been developed in java by Ontoprise (The Ontoprise
GmbH was founded 1999 as a spin off of the University of
Karlsruhe) and it is available in different versions; one of them
is downloadable free from the web site
(http://www.ontoprise.de/customercenter/software\_downloads/free\_en),
other versions provide more services and plugins to enrich its
features.\\
A detailed paper is ``Y. Sure et al.: OntoEdit:
Collaborative Ontology Engineering for the Semantic Web. In
Proceedings of the first International Semantic Web Conference
2002 -- ISWC 2002'' or Deliverable D3 of
On-To-Knowledge\footnote{http://www.ontoknowledge.org/countd/countdown.cgi?del3.pdf}.\\
Further information has been presented also in sec. \ref{s3:ontoedit}.\\

\noindent URL: http://www.ontoprise.de/ontoedit.htm\\

\noindent \textbf{\emph{Ontology Middleware Module}}
(\textbf{OMM}) supports well-defined application programming
interfaces (OMAPI) used for access to knowledge and deals with
such matters as:
\begin{itemize}
    \item Ontology versioning, including branching;
    \item Security -- user profiles and groups are used to control
    the rights for access, modifications and publishing;
    \item Meta-information and ontology lookup -- support for
    meta-properties (such as Status, Last-Updated-By, Responsible,
    Comments, etc.) for whole ontologies, as well as for separate
    concepts and properties. Ontology and concept lookup according to
    meta-information is possible;
    \item Access via a number of protocols: HTTP, RMI, EJB, CORBA, and
    SOAP.
\end{itemize}
The Ontology Middleware Module is an enterprise back-end for
formal knowledge management. It permits to knowledge
representation and management in RDF(S) and languages structurally
compatible with it, such as, DAML+OIL and OWL. OMM is an extension
of Sesame.\\
Further information can be found in Deliverable
D38\footnote{http://www.ontoknowledge.org/downl/del38.pdf}.\\
The Ontology Middleware is open source and is available for
download and contributions at http://sourceforge.net/projects/sesame/\\
It has been developed by the OntoText
Lab\footnote{http://www.ontotext.com/}. On the web site a working demo
version is also present.\\

\noindent URL: http://www.ontotext.com/omm\\

\noindent \textbf{\emph{Sesame}} is a system that permits
persistent storage of RDF data and schema information and
subsequent online querying of that information. One of the most
important modules of Sesame is the query engine. This query engine
supports an OQL-style query language called RQL. RQL supports
querying of both RDF data (e.g. instances) and schema information
(e.g. class hierarchies, domains and ranges of properties). RQL
also supports path-expressions through RDF graphs, and can combine
data and schema information in one query.\\
Sesame has been developed in java by AIdministrator and it needs
to be installed locally; it is open source and can be downloaded
at \\http://sourceforge.net/projects/sesame/\\ Further information
can be found here: ``Jeen Broekstra, Arjohn Kampman, Frank van
Harmelen(2002). \emph{Sesame: A generic architecture for storing
and queryingrdf and rdf schema}. In Proceedings of the First
International SemanticWeb Conference, Horrocks, I. \& Hendler, J.
A. (Eds.), volume 2342 of Lecture Notes in Computer Science.
Springer''\\http://www.cs.vu.nl/~frankh/postscript/ISWC02.pdf\\
On the Sesame web site several documents and also a demo version
of the system are available.\\

\noindent URL: http://sesame.aidministrator.nl\\

\noindent \textbf{\emph{BOR}} provides additional reasoning
services so as to extend the functionality provided by SESAME.
Most of the classical reasoning tasks for description logics (DL)
are available, including realization and retrieval.\\
In other words BOR is a description logics (DL) reasoner. It has
been developed by OntoText in java and it has been integrated in
the Sesame system. It can be downloaded from the web site with
examples and tutorials. Like Sesame, it needs to be installed locally.\\
On-To-Knowledge has studied BOR in Deliverable D40\\
(http://www.ontoknowledge.org/downl/del40.pdf).\\

\noindent URL: http://www.ontotext.com/bor\\

\noindent \textbf{\emph{CORPORUM}} toolset (\textbf{OntoWrapper}
and \textbf{OntoExtract}) performs information extraction and
ontology generation. It is situated in the extraction layer.
CORPORUM has two related tasks: interpretation of natural language
texts and extraction of specific information from free text.
Whereas CORPORUM tools can perform the former process
autonomously, the latter task requires a user who defines business
rules to extract information from tables, (phone) directories,
homepages, etc.\\
These tools allow the user to specify the pages he wants to
analyze specifying many preferences to refine the final result (an
ontology). Tools have been developed as Microsoft COM objects by
CognIt\footnote{http://www.cognit.no/} and need to be installed
locally. Following the link below you can find further documents
and tutorials.\\
A specific paper is ``R.Engels and B.A. Bremdal. \emph{CORPORUM: A
Workbench for the SemanticWeb}. Semantic Web Mining workshop.
PKDD/ECML - 01. Freiburg, Germany,2001''.\\

\noindent URL: http://ontoserver.cognit.no/\\

\noindent \textbf{Language.}\\

\noindent Regarding the language, \textbf{OIL} \textbf{language}
has been studied (cf. D. Fensel et al.: OIL: Ontology
Infrastructure to Enable the Semantic Web, IEEE Intelligent
System, 2001). It was designed to combine frame-like modelling
primitives with the increased expressive power, formal rigor and
automated reasoning services of an expressive description logic.
OIL is ``web enabled'' by having both XML and RDFS based
serializations. As part of the Semantic Web activity of the W3C, a
very simple web-based ontology language had already been defined,
namely RDF Schema. This language only provides facilities to
define class and property names, inclusion axioms for both classes
and properties (subclasses and sub properties), and to define
domain and range constraints on properties. \emph{OIL has been
designed to be a superset of the constructions in RDF Schema and
the syntax of OIL has been designed such that any valid OIL
document is also a valid RDF(S) document when all the elements
from the OIL-namespace are ignored}.\\

For many of the applications, it is unlikely that a single
language will be ideally suited for all uses and all users. In
order to allow users to choose the expressive power which is
appropriate to their application, and to make future extensions
possible, a layered family of OIL languages has been constructed.
The sub-language \emph{OIL Core} has been defined to be exactly
the part of OIL that coincides with RDF(S). This corresponds to
full RDF(S), without some of RDF’s more dubious constructions:
containers and reification.\\During the project it was observed
that \emph{some problems arise using OIL}. In fact in the case
studies they worked on, the domain experts often did not employ
the features of OIL to construct ontologies, they simply referred
to RDF(S) or to Core-OIL. Very lightweight ontologies were
constructed, sometimes consisting of not much more than taxonomies
of terms, often with no properties relating them. Similarly, the
automatic concept-extraction technology that was employed also
yielded very light-weight collections of terms, with almost no
hierarchical structure and with only very general associations
between the concepts (of the kind: ``Concept1 isStronglyRelatedTo
Concept2''). They try to explain these problems and they state
that:
\begin{itemize}
    \item Some case studies already started even before the first
    version of OIL was available;
    \item OIL lacks any tutorial support, any customized tool support,
    and any real practical experience. From a user point of view this
    makes it nearly unusable for the moment;
    \item OIL is ongoing standardization efforts including high change
    rates. Neither the user nor the tool developer can trust the current language
    version as being final and not just an intermediate step. Using
    OIL is of high risk, both for users in case studies as well as
    for tool developers.
\end{itemize}

\noindent \textbf{Methodology.} \\

\noindent Finally, a methodology for employing ontology-based
tools for knowledge management applications was developed. They
prepared an initial baseline methodology and apply it to the case
studies they worked on. This methodology provides guidelines for
introducing knowledge management concepts and tools into
enterprises, helping knowledge providers and seekers to present
knowledge efficiently and
effectively.\\

\noindent At the end of the project they published a book to
present and promote their outcomes (``Towards the Semantic Web.
Ontology-Driven Knowledge Management''. J. Davies, D. Fensel, F.
van Harmelen, Wiley ed.). They contributed to the creation of the
first International Semantic Web Conference (ISWC) that took place
in June 2002 in Sardinia (Italy) and they will organize the second
one in October 2003 in Florida
(USA).\footnote{http://iswc.semanticweb.org/}

The researchers that worked at the On-To-Knowledge project are
strongly involved in creating the international standard called
OWL described before. In co-operation with the large US project
DAML\footnote{http://www.daml.org/} they developed a joined
consensual sub-dialect of OIL called DAML+OIL.

%
% fine On-To-Knowledge
%

\section{MAFRA} \label{MAFRA} \textbf{MAFRA} -- \textbf{A Mapping FRAmework for
Distributed Ontologies} \cite{MaedcheEtAl2002} developed at the
University of Karlsruhe. \emph{It is an interactive, incremental
and dynamic framework for mapping distributed ontologies}.

As we have seen also before, the de-centralized nature of the Web
makes indeed inevitable that communities will use their own
ontologies to describe their data. In this vision, ontologies are
themselves distributed and the key point is the mediation between
distributed data using mappings between ontologies. Thus, complex
mappings and reasoning about those mappings are necessary for
comparing and combining ontologies, and for integrating data
described using different ontologies.

Following the MAFRA approach, \emph{an ontology mapping process is
the set of activities required to transform instances of a source
ontology into instances of a target ontology}. The framework, they
are building, consists of five horizontal modules describing the
fundamental phases of mapping process. Four vertical components
run along the entire mapping process, interacting with horizontal
modules (see Figure \ref{f:Mafra}).
\begin{figure}[t]
  \begin{center}
  \epsfig{figure=Mafra.eps,height=8cm,angle=0}
  \caption{MAFRA conceptual architecture}\label{f:Mafra}
  \end{center}
\end{figure}

Within the horizontal dimension, there are the following five
modules:\\

\noindent \textbf{\emph{Lift and Normalization.}} This module
focuses on raising all data to be mapped onto the same
representation level, coping with syntactical, structural and
language heterogeneity. Both ontologies must be normalized to a
uniform representation, that is RDF(S), thus eliminating syntax
differences and making semantics differences between the source
and the target ontology more apparent. The source and target
ontologies are already represented in RDF-Schema with their
instances in RDF. An essential step of this first phase is also
normalization. Three distinct ordered tasks are performed in our
approach: (i) tokenization of the entities, (ii) elimination of
resulting stop words and (iii) expansion of acronyms. The result
is a list of normalized lexicon.\\

\noindent \textbf{\emph{Similarity.}} This module establishes
similarities between entities from the source and target ontology,
thus, it supports mapping discovery. Several different similarity
measures have been proposed in literature. They adopted a
multi-strategy process, that calculates similarities between
ontology entities using different algorithms.\\

\noindent \textbf{\emph{Semantic Bridging.}} Based on the
similarities computed in the previous phase, the semantic bridging
phase is responsible for establishing correspondence between
entities from the source and target ontology. It intends to
specify bridges between entities in a way that each instance
represented according to the source ontology is translated into
the most similar instance described according to the target
ontology.\\

\noindent \textbf{\emph{Execution.}} This module actually
transforms instances from the source ontology into target ontology
by evaluating the semantic bridges defined earlier. In general two
distinct modes of operation are possible, namely offline (static,
one-time transformation) and online (dynamic, continuous mapping
between source and the target) execution.\\

\noindent \textbf{\emph{Post-processing.}} The post-processing
component takes the results of the execution module to check and
improve the quality of the transformation results. The most
challenging task of post-processing is establishing object
identity-recognizing that two instances represent the same
real-world object.\\

The vertical dimension of MAFRA contains modules that interact
with horizontal modules during the overall mapping process. Four
modules have been identified.\\

\noindent \textbf{\emph{Evolution.}} This aspect focuses on
keeping semantic bridges obtained by the ``Semantic Bridge''
module, which must be kept in synchrony with the changes in the
source and target ontologies.\\

\noindent \textbf{\emph{Cooperative Consensus Building.}} The
cooperative consensus building aspect is responsible for
establishing a consensus on semantic bridges between two
communities participating in the mapping process. This is a
requirement as one has to choose frequently from multiple,
alternatively possible mappings .The amount of human involvement
required to achieve consensus may be reduced by automating the
mapping process as much as possible.\\

\noindent \textbf{\emph{Domain Constraints and Background
Knowledge.}} The quality of similarity computation and semantic
bridging may be dramatically improved by introducing background
knowledge and domain constraints, e.g. by using glossaries to help
identify synonyms or by using lexical ontologies, such as
\mbox{WordNet} or domain-specific thesauri, to identify similar
concepts.\\

\noindent \textbf{\emph{Graphical User Interface.}} Mapping is a
difficult and time consuming process, which is not less difficult
than building an ontology itself, i.e. deep understanding of both
conceptualizations required on human side, thus extensive
graphical support must be given and it is a separate issue how
this can be achieved in an optimal way.\\

\clearpage \noindent \textbf{MAFRA} is currently under development
within the \textbf{KAON Ontology and Semantic Web Framework}.
\textbf{KAON}\footnote{http://kaon.semanticweb.org} is the
Karlsruhe Ontology and Semantic Web framework, it has been
developed at the University of Karlsruhe (Germany) and is used as
a basis for several ontology-enabled research and industry
projects. It’s primary goal is to establish a platform needed to
apply Semantic Web technologies to e-commerce scenarios, knowledge
management, automatic generation of Web portals, E-Learning etc.
Till now they achieved the implementation of four modules of
MAFRA: the automatic similarity discovery module, the semantic
bridging representation, the graphical user interface and the
execution engine.\\

\noindent Navigating the KAON web portal you can find all the
updated information about the evolution of modules
(http://kaon.semanticweb.org/modules) and download them
(http://kaon.semanticweb.org/download). Tools have been developed
in Java. There is also an interesting section of the site where it
is possible to find demos of KAON
(http://kaon.semanticweb.org/demos). Moreover there are several
useful ontologies to download for testing or using with tools
(http://kaon.semanticweb.org/ontologies). Further information such
as tutorials, papers, and links to other projects that use KAON
are available in the \emph{documentation, papers} and
\emph{projects} sections of the site.


\section{SHOE} \label{SHOE}
The \textbf{Simple HTML Ontology Extension}
(\textbf{SHOE}\footnote{http://www.cs.umd.edu/projects/plus/SHOE/index.html})
\emph{is an HTML-based knowledge representation language}
developed at the Computer Science Department of the Maryland
University. SHOE is a superset of HTML which adds the tags
necessary to embed arbitrary semantic data into
web pages \cite{shoe}.\\

% Parte aggiunta da controllare
The underlying philosophy of SHOE is that intelligent internet
agents will be able to better perform their tasks if the most
useful information is provided in a structured manner. To this
end, \emph{SHOE extends HTML} with a set of knowledge oriented
tags that, unlike HTML tags, provide structure for knowledge
acquisition as opposed to information presentation. \emph{SHOE
associates meaning with this content by making each web page
commit to one or more ontologies}. These ontologies permit the
discovery of implicit knowledge through the use of taxonomies and
inference rules, allowing information providers to encode only the
necessary information on their web pages, and to use the level of
detail that is appropriate to the context. Interoperability is
promoted through the sharing and reuse of ontologies. To achieve
compatibility with existing web standards, SHOE’s syntax is
defined as an application of SGML, a language that defines
tag-based languages and was the influence for HTML’s syntax. A
slight variant of the syntax exists for compatibility with XML,
and can be used by web sites that have begun to migrate to XML.\\
% Fine parte aggiunta

\noindent \emph{SHOE tags} are divided into two categories. First,
there are \emph{tags for constructing ontologies}. SHOE ontologies
are sets of rules which define what kinds of assertions SHOE
documents can make and what these assertions mean. For example, a
SHOE ontology might say that a SHOE document can declare that a
data entity is a ``dog'', and that this ``dog'' is permitted to
have a ``name''. Secondly, there are \emph{tags for annotating web
documents to subscribe to one or more ontologies, declare data
entities, and make assertions about those entities under the rules
proscribed by the ontologies}. For example, a SHOE document
subscribing to the SHOE ontology above, might then declare that it
is all about a dog named ``Fido''.\\
%
% Parte tolta perchè poco significativa
%
%SHOE was designed with the needs of the web in mind. It has
%limited semantics to make it possible to handle large amounts of
%data. However, simple database semantics are not enough for web
%data; SHOE provides true knowledge-base semantics. It has a
%variety of mechanisms that try to deal with the fact that the data
%out there is distributed and under no one's total control.

SHOE can be used to embed data from a variety of sources and for a
variety of purposes. It is not intended for any particular
function. However, SHOE is primarily meant to make it possible for
web robots and intelligent agents to finally simplify the actions
we perform every day.

SHOE is not just a meta-content language. SHOE provides a
relatively rich level of semantics and abilities, which enable web
designers to embed documents not only with information about the
overall ``content'' of those documents but any arbitrary
information at all. SHOE also allows agents to make automatic
inferences about the data they learn, provides a hierarchical
categorization scheme, and a sophisticated ontology mechanism
designed specifically for the web needs. SHOE tags can be used for
a wide range of agent-based functions.\\
%
% Parte tolta perchè poco significativa
%
%SHOE is purposely not a verbose knowledge-representation system.
%The full semantic expressiveness found in languages such as KIF
%are inappropriate for SHOE because their computational complexity
%is too high. SHOE attempts to provide as rich expressivity as
%possible while keeping in mind that there's a tremendous amount of
%data out there.

SHOE does not have any pre-defined ontologies, categories,
relationships, or inferences. SHOE is a language in which
categories, relationships, attributes, inferences, etc. can be
defined by ontologies, but SHOE itself does not define them. This
is the job of ontology designers for specific tasks or domains.
However, the SHOE project offers some initial ontologies to start.
SHOE Ontologies declare:
\begin{itemize}
    \item Classifications (categories) for data entities. Classifications
    may inherit from other classifications (``Dogs are Animals'').
    \item Valid relationships between data entities and other data
    entities or simple data (strings, numbers, dates, boolean).
    Arguments for relationships are typed, either by the simple
    data that can fill the argument, or with the classification a
    data entity must fall under in order to fill an argument
    (``Dogs can chase cats'').
    \item Inferences in the form of horn clauses with no negation
    (``If a person works for an organization, that person automatically
    works for any organization the organization is a sub-part of'').
    \item Inheritance from other ontologies: ontologies may be derived
    from or extend zero or more outside ontologies (``The SPCA ontology
    extends the common Library of Congress ontology'').
    \item Versioning. Ontologies may extend previous ontology versions.
\end{itemize}
HTML pages with embedded SHOE data may:
\begin{itemize}
    \item Declare arbitrary data entities. Usually, one of these entities is
    the web page itself.
    \item Declare the ontologies which they will use when making declarations
    about entities (``I'm using the 'Pets' ontology promulgated by the SPCA'').
    \item Categorize entities (``This entity is a dog'').
    \item Declare relationships between entities or between entities and data
    (``This entity likes to chase that entity'', ``This entity's name is 'Fido''').
\end{itemize}
SHOE permits n-ary relations, simple inheritance in the form of
classification, multi-valued relations, and a conjunctive
knowledge base. It does not currently permit negation,
disjunction, or arbitrary functions and predicates. In order to
add SHOE to web pages there are two methods: I) you can use the
``Knowledge Annotator'' that is a tools developed to help user
that are not familiar with the HTML and the SHOE language; II) you
can add by yourself the right tags into your web (html) pages
according to SHOE specifications
(http://www.cs.umd.edu/projects/plus/SHOE/spec.html). Here we show
a simple example of a SHOE ontology.

\begin{verbatim}
<HTML>
...
<BODY>
<ONTOLOGY ID="cs-dept-ontology" VERSION="1.1"
                BACKWARD-COMPATIBLE-WITH="1.0">
<USE-ONTOLOGY ID="univ-ontology" VERSION="1.0" PREFIX="u"
                URL="http://ontlib.org/univ_v1.0.html">
...
<DEF-CATEGORY NAME="ComputerScience" ISA="u.ResearchArea">
...
<DEF-RELATION NAME="writtenIn">
    <DEF-ARG POS=1 TYPE="Program">
    <DEF-ARG POS=2 TYPE="ComputerLanguage">
</DEF-RELATION> ... <DEF-RENAMEFROM="u.Department"TO="Department">
<DEF-RENAME FROM="u.Chair" TO="DepartmentHead">
...
</ONTOLOGY>
</BODY>
</HTML>
\end{verbatim}\\

The researchers of SHOE have recently moved to the \textbf{MIND
SWAP}\footnote{http://www.mindswap.org/} (Maryland Information and
Network Dynamics Lab - Semantic Web Agents Project) of the
University of Maryland. They use OWL and DAML+OIL languages that
are in part based on SHOE.

On both web sites (SHOE and MIN SWAP) different tools are
available to download: web pages annotator (SMORE, Knowledge
Annotator, RIC), converter (ConvertToRDF, OWL Converter,
Excel2RDF). The tools have been developed using Java. Moreover
they have annotated web pages and they propose a search engine
that performs semantic searches exploiting
SHOE features.\\

\section{Comments} \label{c5:comments}
We begin observing that many projects are on going with respect to
the new issue of the Semantic Web but we think that there is not a
clear and common objective; in some way, this fact creates some
confusion in the research environment. Probably the causes of such
a situation are that, on one hand, the idea of Semantic Web has
created a lot of expectations but on the other hand this idea has
not specified what it is necessary to become concrete. Thus the
research has followed different trails instead of concentrating on
few clear requirements or components to build the Semantic Web.\\

The projects and the studies that OntoWeb and On-To-Knowledge are
conducting, are heterogeneous. Specially the tools that
On-To-Knowledge has developed, cooperate to realize their
architecture, but they do not form a complete environment for the
management of all the aspects related to the dynamics of
ontologies. Moreover the tools have been developed by many
research groups and therefore they are quite different.\\

From our point of view this fragmentation is a problem,
because our first objective is to build a complete infrastructure
that can lead the users during all the life cycle of ontologies,
from their creation, through their exploitation, their evolution,
their interrogation. Concerning this we can say that the study of
related projects has been useful to acquire important knowledge
about the management of ontologies and to elaborate our proposal.

\vspace{0.3cm}With respect to the results of MAFRA we can observe
that the approach they have chosen is very interesting. They try
to build a framework that helps the users in all the phases of
ontologies' management. We remember that they use a mapping
approach to solve the problem of distributed ontologies, while our
objective is to construct a common and shared ontology and use
this integrated view to look at the represented domain.\\

Finally we have languages. As we have seen before, On-To-Knowledge
researchers have encountered problems using the new language they
studied, OIL, and they have tried to explain some causes. We are
convinced that also SHOE suffers the same problems. In fact, the
researchers have to be sure that the language is stable or better,
standard in order to develop tools and models that can rely on it.
Moreover both OIL and OWL (its successor) have more than one level
of use; this flexibility is necessary to help domain experts
providing them with the most suitable level of details, during the
modelling phase. Probably researchers will change their language
only when a new language will reach a sure status of
standardization and the improvements will be tested clearly.

\vspace{0.3cm}In consequence of these drawbacks related to new
languages, we decided to keep \lang~ and XML as base language to
represent and to export our data.

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