\chapter{The Semantic Web}
\label{c:TSW}
%PANORAMICA SUL SEMANTIC WEB
\section{Introduction}
\label{s1:introduzione}
\par In this chapter I will present the new scenarios introduced
by the Semantic Web. A fundamental article about the Semantic Web
is the one written by J. Hendler, O. Lassila and T. Berners-Lee
that appeared on \mbox{Scientific
America}\footnote{http://www.sciam.com/} in the 2001
\cite{Semantic2001}. The authors depicted new ``world'' where
people interoperate with their electronic devices in a very
different way from today. They imagine that, for instances, you
are at the doctor's office and you have to book a series of
therapy sessions but you already had some appointments. You can
instructed your Semantic Web agent through your handheld Web
browser. You can set up the search to find only places within a
20-km radius of your home and only in a specific time of the day.
The agent, then, starts to look for information interacting with
other agent present on the web sites of doctors in the area. In
few minutes the agent presents you with a plan; if you like it you
can change your week agenda and mail your tennis trainer that for
a month you can not play. If you don't like the plan you can set
your agent with stricter preferences and redo the search.
\par Unfortunately today the World Wide Web can not carry
out all these tasks but probably the Semantic Web that it will
evolve into tomorrow. Most of the Web's content today is designed
for humans to read, not for computer programs to manipulate
meaningfully. Computers can easily parse Web pages for layout and
routine processing -- a header, a link to another page -- but in
general they have no reliable way to process the semantics: this
the home page of my professor and this link goes to the course
materials page.

\section{What is the Semantic Web?}
\label{sec:SW}
%DESCRIZIONE WEB SEMANTICO
\par Nowadays the Web is a huge collection of information useful
for people but meaningless for machines without a post-processing.
By now the WWW reaches its second version; the first saw the HTML
static pages changing the style people share information, while
the second is the one we are experiencing today with dynamic pages
created as response to a query on a database. The increasing of
available information and of the usage of the Web are creating new
expectations with the users. In other words it's time to think
about the third version of the WWW: \emph{the Semantic Web}.
\par \emph{The Semantic Web} will bring structure to the meaningful content
of Web pages, creating an environment where software agents,
roaming from page to page can readily carry out sophisticated
tasks for users. Such an agent coming to the professor's Web page
will not just know that the page has some keywords such as
``ontology, knowledge representation, artificial intelligence''
(as might be encoded today) but also that the course is scheduled
for Tuesday and Thursday at 11.00 am. These semantics were encoded
into the Web page using off-the-shelf software for writing
Semantic Web pages.
\par The Semantic Web is not a separate Web but an extension of
the current one, in which information is given well-defined
meaning, better enabling computers and people to work in
cooperation. The first steps in weaving the Semantic Web into the
structure of the existing Web are already under way. In the near
future, these developments will introduce new functionality as
machines become much better able to process and ``understand'' the
data that they merely display at present.
\\ The essential property of the World Wide Web is its universality.
The power of a hypertext link is that ``anything can link to
anything''. Web technology, therefore, don't discriminate between
commercial and academic information, or among cultures, languages,
media and so on. Information varies along many axes. One of these
is the difference between information produced primarily for human
consumption and that produced mainly for machines. At one end we
have everything from the TV commercial to poetry. At the other end
we have databases, programs and sensor output. Today, the Web has
developed most rapidly as a means of documents for people rather
than for data and information that can be processed automatically.
The Semantic Web aims to build this capabilities. Like the
Internet, the Semantic Web will be as decentralized as possible.
Such Web-like systems generate a lot of excitement at every level,
from major corporation to individual user, and provide benefits
that are hard or impossible to predict in advance.

\subsection{Knowledge representation}
%Rappresentazione della conoscenza
\label{sec1:KR}
For the semantic web to function, computers must
have access to structured collections of information and sets of
inference rules that they can use to conduct automated reasoning.
Artificial-intelligence researchers have studied such systems
since long before the Web was developed. Knowledge representation,
is currently in a state comparable to that of hypertext before the
advent of the Web. Many studies are been conducted in this area
but till now there are only some demonstrations of the numerous
potential.
\\Traditional knowledge-representation systems typically have been
centralized, requiring everyone to share exactly the same
definition of common concepts. But central control is increasing
the size and scope of such a system and rapidly it becomes
unmanageable.
\\Semantic Web researchers make the language for the rules as
expressive as needed to allow the Web to reason as widely as
desired. The challenge of the Semantic Web, therefore, is to
provide a language that expresses both data and rules for
reasoning about the data and that allows rules from any existing
knowledge-representation system to be exported onto the Web.
\\Adding logic to the Web -- the means to use rules to make
inferences, choose courses of action and answer question -- is a
fundamental task within the Semantic Web community at the moment.
Two important technologies for developing the Semantic Web are
already in place: eXtensible Markup Language (XML) and the
Resource Description Framework (RDF). XML lets everyone create
their own tags -- hidden labels that annotate Web pages or
sections of text on a page. Scripts, or programs, can make use of
these tags in sophisticated ways, but the scripts writer has to
know what the page writer uses each tag for. In other words, XML
allows users to add arbitrary structure to their documents but
says nothing about what the structures mean.
\\Meaning is expressed by RDF, which encodes it in \emph{sets of
triples}, each triple being like subject, verb and object of an
elementary sentence. These triples can be written using XML tags.
In RDF, a document makes assertions that is, particular things
(people, Web pages or whatever) have properties (``is a sister
of'', ``is the author of'') with certain values (another person,
another Web page). This structure turns out to be a natural way to
describe the vast majority of the data processed by machines.
Subject and object are each identified by a Universal Resource
Identifier (URI), just as used in a link on a Web page. The verbs
are also identified by URIs, which enables anyone to define new
concept, a new verb, just by defining a URI for it somewhere on
the Web.

\subsection{Ontologies}
%Ontologie
\label{sec1:Ontologie} In such a system, where information are
distributed in different Web sites or databases, a program that
wants to compare or combine information across two databases has
to solve the problem of information integration. For example the
word ``staff'' in a DB may reference to the persons who work in a
certain department while another DB may call them ``employee''.
Ideally, the program must have a way to discover such common
meanings for whatever databases it encounters. A solution to this
problem is provided by the third basic component of the Semantic
Web, collections of information called \emph{ontologies}.
Artificial-intelligence and Web researchers use this term to
identify a document or file that formally defines the relations
among terms. The most typical kind of ontology for the Web has a
taxonomy and a set of inference rules. The taxonomy defines
classes of objects and relations among them. For example, an
address may be defined as a type of location, and city codes may
be defined to apply only to locations, and so on. Classes,
subclasses and relations among entities are a very powerful tool
for Web use. We can express a large number of relations among
entities by assigning properties to classes and allowing
subclasses to inherit such properties.
\par Inference rules in ontologies supply further power. An
ontology may express rules on the classes and relations in such a
way that a machine can deduce some conclusions. The computer
doesn't truly ``understand'' any of this information, but it can
now manipulate the terms much more effectively in ways that are
useful and meaningful to the human user. With ontology pages on
the Web, solutions to terminology (and other) problems begin to
emerge. The meaning of terms or XML codes used on a Web page can
be defined by pointers from the page to an ontology.
\par Ontologies can enhance the functioning of the Web in many
ways. They can be used in a simple fashion to improve the accuracy
of Web searches--the search program can look for only those pages
that refer to a precise concept instead of all the ones using
ambiguous keywords. More advanced applications will use ontologies
to relate the information on a page to the associated knowledge
structures and inference rules.
\par A more precise and large description of ontologies will be
presented in chapter two.

\subsection{Agents}
%Agenti
\label{sec1:Agenti} The real power of the Semantic Web will be
realized when people create many programs that collect Web content
from diverse sources, process the information and exchange the
results with other programs. The effectiveness of such software
agents will increase exponentially as more machine-readable Web
content and automated services (including other agents) become
available. In fact, an agent is a software component that enjoys the
following properties [citare  M. Wooldridge and N.R. Jennings, Intelligent
Agents: Theories and practice. The Knowledge Engineering Review,
10(2):115-152, 1995]:
begin{itemize}
\item autonomy: agents encapsulate some state and make decision about what
  to do based on this state, without any human interaction
\item reactivity: agents are situated in an environment, they are able to
  perceive the changements of the environment, and they can respond to those
  changements
\item pro-activeness: agents are able to exhibit goal-directed behaviuor by
  taking the initiative
\item social ability: agents interact with other agents and with other applications.
end {itemize}


Due to the described features, an agent can automatically navigate in the
Web and move from a site to an other site in order to accomplish a specific
task. If the web where the agent navigates is a Semantic Way, an agent may
have the perception of the contents stored in the visited sources. In this
way, it can change its behavior on the base of the visited sites, and, for
exaple, it can follow specific links because they are semantically correct
with respect to the goal of the agent. Therefore, the Semantic Web allows an
agent to have a well-knowledge of the environment where the agent is
instantiate and consequently to react to the environment without mistakes.



%PROGETTI INTERNAZIONALI
\section{International projects}
\label{sec:International} In this section I will present some
international projects of particular interest for my thesis. All
around the world several universities, organizations, research
communities started projects related to \emph{the Semantic Web}.
First of all the W3C consortium\footnote{http://www.w3c.org/sw}
has a particular group of people that are studying  new languages
(OWL \emph{Web Ontology Language}) and new standards related to
the new tools that start to appearing on the web. Also some
important universities are involved in the study of the problem.
The Standford University is developing interesting tools for the
management of ontologies (Proteg\'e2000). The Karlsruhe University
is working on another framework for the management of the dynamic
of ontologies (Kaon). Inside the UE the are different projects
(OntoWeb, On-To-Knowledge, SemanticWeb) that are involved in the
same area of interest. Now I explain in a more detailed way some
of the cited projects.

\subsection{OntoWeb}
\label{ontoweb} OntoWeb\footnote{http://www.ontoweb.org}:
Ontology-based information exchange for Knowledge Management and
Electronic Commerce. OntoWeb is a Thematic Network founded by the
European Commission. It started in June 2001 and it will end in
May 2004. It's contract number is \mbox{IST-2000-29243}

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 for providing various
services. Ontologies help people and computers to access the
information they need, and effectively communicate with each
other. Therefore they have a crucial role to play in enabling
content-based access, interoperability, and communication across
the Web, providing it with a qualitatively new level of service:
the Semantic Web. It weaves together a net linking incredible
large parts of the human knowledge and complements it with machine
processability. The goal of the \mbox{OntoWeb} Network is to bring
together researchers and industry to promote interdisciplinary
work on the Semantic Web.

Another important aspects on which the project relies is the
competitiveness of companies active in areas. The business world
has a high rate of change so it's of crucial importance how
effectively the companies acquire, maintain, exchange and access
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 increasingly 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 increasing international
trading of products and services. The ability to find, interrogate
and exchange knowledge is fundamental for B2B and B2C e-Commerce.
OntoWeb will identify and publicize the ontolgy-based technologies
required for the promotion of knowledge management and e-Commerce.

The \mbox{OntoWeb} Network will be the European focal point for
bringing 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
strategic guide to industrial and commercial applications.
    \item Cooperating with different 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 Special Interest Groups (SIGs) are the following:
\begin{itemize}
    \item \emph{Content Standards} (SIG1). It's coordinated by Nicola
    Guarino (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, push 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{Ontology Language Standards} (SIG2). It's
    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{Enterprise-Standard Ontology Environments} (SIG3).
    The responsible persons for this SIG are Asunci\'on G\'omez-P\'erez
    (University of Marid, Spain) Mike Brown (SemanticEdge, Germany).
    They try to support a dialog for the development of Quality Requirements
    for enterprise-standard ontology environments. They promote the
    efficacious 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{Industrial Applications} (SIG4). I's guided by
    Hans Akkermans (Vrije Universiteit Amsterdam, the
    Netherlands). They provide open forum to bridge the potential
    ontology-related researches with the current electronic commerce
    market, for instance, B2C, B2B.
\end{itemize}

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

IT, and especially Internet/WWW, have boosted potential for
knowledge acquisition and sharing but information resources are
heterogeneous, distributed, semi-structured, and enormous in size.
So the need for tools for selective semantic (meaning-oriented)
access. On-To-Knowledge wants to improve access to information
sources and to lower costs for providing and maintaining large
bodies of textual and semistructured information. In order to
reach that objectives they prepare tools for Ontology Construction
and Interchange, they build up infrastructure for Information
representation, they refine the means for Information access and
extraction. During the project they analyze three large case
studies in order to develop the technology according to actual
needs of large and/or virtual organizations.

The project now is finished and in the last
deliverable\footnote{On-To-Knowledge Final Report} they describe
they achievements. Ontoknowledge 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 I try to summarize some
results.

\textbf{Tools.} A major objective of the project is to create
intelligent software to support users in both accessing
information and in the maintenance, conversion, and acquisition of
information sources. These tools are based on a three layered
architecture around information access, information storage, and
information generation.

\ldots

\subsection{SHOE}
\label{SHOE} SHOE is an HTML-based knowledge representation
language. SHOE is a superset of HTML which adds the tags necessary
to embed arbitrary semantic data into web pages.

SHOE tags are divided into two categories. First, there are 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 some data entity is a
``dog'', and that if it does so, that this ``dog'' is permitted to
have a ``name''. Secondly, there are 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's all about a dog named ``Fido''.

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 one particular
function. However, SHOE is primarily meant to make it possible for
web robots and intelligent agents to finally make a dent in making
all our lives a little easier.

What SHOE Isn't...

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.

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 does offer some initial ontologies to
start things rolling, though we hope these ontologies will
ultimately be superseded by other more widely-accepted ontologies.

SHOE's semantics are intentionally very simple. SHOE Ontologies
declare:

Classifications (categories) for data entities. Classifications
may inherit from other classifications (``Dogs are Animals'').
Valid relationships between data entities and other data entities
or simple data (strings, numbers, dates, booleans). 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'').
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'').
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''). Versioning.
Ontologies may extend previous ontology versions. HTML pages with
embedded SHOE data may:

Declare arbitrary data entities. Usually, one of these entities is
the web page itself. Declare the ontologies which they will use
when making declarations about entities (``I'm using the 'Pets'
ontology promulgated by the SPCA''). Categorize entities (``This
entity is a dog''). Declare relationships between entities or
between entities and data (``This entity likes to chase that
entity'', ``This entity's name is 'Fido'''). SHOE allows n-ary
relations, horn clause inference, simple inheritance in the form
of classification, multi-valued relations, and a conjunctive
knowledge base. It does not currently allow negation, disjunction,
or arbitrary functions and predicates.

SHOE attempts to make it difficult for entities to pretend to be
other entities by providing an easily verifiable key scheme based
on URLs.

\subsection{MAFRA}
\label{MAFRA} MAFRA -- A Mapping FRAmework for Distributed
Ontologies\cite{MaedcheEtAl2002}. It's 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.

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 in a
mapping process. Four vertical components run along the entire
mapping process, interacting with horizontal modules.

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

\emph{Lift \& 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, in our
case 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. Also one
essential step of this first phase is 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 lexica.

\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.

\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.

\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.

\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.

\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.

\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.

\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.

\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.

\subsection{OntoView}
\label{OntoView} Framework for versioning by Fensel \& Co.

\ldots

\subsection{Semantic Web}
\label{OntoView} The Semantic Web Portal.

\ldots

\subsection{W3C Consortium}
\label{OntoView} The Semantic Web section of W3C consortium.

\ldots


\section{SEWASIE}
\label{s1:Sewasie}
\subsection{Overview} SEWASIE\footnote{http://www.sewasie.org/} stands for
``SEmantic Webs and AgentS in Integrated Economies''. It is an
European project inserted in the $5^{th}$ Framework IST programme
of the European Communities within the Semantic Web Action Line.

SEWASIE will design and implement an advanced search engine
enabling intelligent access to heterogeneous data sources on the
web via semantic enrichment to provide the basis of structured
secure web-based communication.

The objectives are to develop a distributed agent-based
architecture of semantic search and communication using
community-specific multilingual ontologies; to equip ontologies
with an inference layer grounded in W3C standards; to develop
prototypes that meet the needs of SMEs in a EU context; to obtain
practical experience (user requirements, potential added value,
risks etc.). In particular the programme focuses on the question
how to assist networks of small and medium enterprises (so-called
Integrated Economies) in enhancing their intra-and
inter-organisational information management capabilities. The
project also includes novel techniques for semantic enrichment,
query management, and presentation techniques in multi-lingual
information acquisition from the web.


\subsection{The Business Scenario} Throughout Europe, much of the
industrial fabric is made of small and medium-sized agricultural,
manufacturing, commercial, and services firms (SMEs). For social
and historical reasons, they often aggregate into sectorial
clusters (that the economic literature names as industrial
districts); nowadays, this kind of economic organization is made
vulnerable by the globalisation and it will be more so in the
future.
\par In such a condition, to find (the adequate supplier, an
innovative working method, a new market, and so on) and to be
found (by possible customers, partners or sponsors) makes the
difference; the current Internet tools (e.g. search engines) are
inadequate since they are very difficult to use (small firms do
not necessarily have the required technological infrastructure and
know-how) and because a simple query produces pages and pages of
links, most of them worthless. Suppose that a firm needs to know
about a topic (a product, a supplier, a fashion trend, a norm, and
so on): generally they have to make several quests. For example,
if they look for a new ``fabric dyeing process'' they need to find
who produces it and if there exist specific norms about the
disposal of the dyeing waste material. First of all they have to
make a quest using the term ``fabric dyeing''. Clearly, the search
engine will list links (540 in the case of www.google.com)
concerning not only manufacturers of fabric dyeing equipment, but
also the history of dyeing, the dyeing technology, and so on.
After having located a possible supplier, the company must find
relevant laws and norms about waste disposal and their related
interpretations. The search criteria can be, for example, the
number of the law, the term ``law'', the interpretation etc. Both
searches become more difficult when searching abroad, since there
can exist a specific terminology that the user might not be
familiar with.
\par Suppose instead that the user is a business
professional, and (s)he is interested only in the normative part.
In this case (s)he needs to retrieve rapidly not only the relevant
laws, but also references, connected issues and so on; all this by
searching both on free and subscribed web sites.
\par For this reason, users need to have at their disposal an
engine equipped with an easy-to-use query interface able to
extract the required information from the Internet and to show it
in an easily enjoyable format. If a step-by-step approach is
allowed, the following deductive reasoning steps are easy as well.
Easy-to-use means ``usable by lay users and by means of poor
infrastructures''.
\par The main requirement is to get structured
results obtained from an interpretation of vague queries followed
by some filtering techniques based on designer rules and the
acquired experiences. For instance, starting from a request simply
constituted by the keyword ``punch'' the engine should answer with
one or more documents containing information in an
easy-to-accessible format such as sellers, prices, manufacturers,
technical literature on its use, importer and so on. Particularly
interesting for products and manufactures is also to know if some
stock goods at low price exist or if there are any auctions or
other negotiation mechanisms for the construction or the purchase
(from e-procurement sites).


\subsection{The SEWASIE Idea} The goal of the SEmantic Webs and
AgentS in Integrated Economies project is the design and the
implementation of an advanced search engine allowing information
access via a machine-processable semantics of data in order to
provide the basis of structured web-based communication. Tools and
methods will be developed to create/maintain multilingual
ontologies, with an inference layer grounded in W3C standards
(XML, XML Schema, RDF(S)), that are the basis for the advanced
search mechanisms and that provide the terminology for the
structured communication exchanges. Search results will be
personalised and visualised according to users' preferences.
SEWASIE will provide an open and distributed architecture based on
mobile, intelligent agents (brokers, mediators and wrappers)
facing scalability and flexibility issues, i.e. the ability to fit
in changing and growing environments and to interoperate with
other systems, while offering one central point of access to the
user. The user will be supported in querying heterogeneous web
information sources. The user query will be sent to a query agent
that processes and answers the query moving through the SEWASIE
information nodes in order to retrieve the information requested
by the user (see \mbox{figure \ref{SVN}}). Information nodes are
independent components that semantically enrich existing data
sources by linking the data to ontologies and other metadata. The
\mbox{SEWASIE} project will also allow for a real life business
evaluation of the results, striving to develop a system and tools
which will not only solve the problem, but do so in a usable,
marketable way.
\begin{figure}[ht]
  \begin{center}
    \epsfig{figure=sewasiefigure1.eps,width=12cm,angle=0}
    \caption{The SEAWSIE Virtual Network}\label{SVN}
  \end{center}
\end{figure}
\par In particular, the SEWASIE project will pursue the following
aims:
\begin{itemize}
    \item To develop an agent-based secure, scalable and distributed
system architecture for semantic search (ontology based) and for
structured web-based communication (for electronic negotiation).
    \item To develop a general framework responsible for the implementation
of the semantic enrichment processes leading to the semantically
enriched virtual data stores, which constitute the information
nodes accessible by the users. The created ontology will be
multilingual accessible, founded on a logical layer and coded
using widespread W3C standards.
    \item To develop a general framework for query management and
information reconciliation taking into account the semantically
enriched data stores. In particular, first commonalities among
queries will be detected, then the relevant virtual data stores
responsible for answering parts of the queries determined and the
queries accordingly splitted, finally the sub-answers will be
combined together in order to provide the user with an overall
answer to the original query.
    \item To develop an information brokering component which will include
methods for collecting, contextualising and visualising
semantically rich data. To obtain this result, intelligent
information filtering and knowledge guidance services will be
developed on the basis of semantic web technologies; structured
data will be linked to semi- or unstructured data via ontologies;
for financial controlling applications collected data will be
visualised to show related documents and search result contexts.
    \item To develop structured communication processes that enable the use
of ontologies. The communication tool will enable structured
negotiation support for human negotiators engaging in
business-to-business electronic commerce and employing intelligent
software agents for some routine communication task.
    \item To develop end-user interfaces for both the semantic design and
the query management. The first is a tool supporting the design,
the management, and the storage of the semantic information
associated to virtual data stores together with a conceptual
modelling methodology associated to the devised data model. The
latter is a tool for end-user query management and intelligent
navigation exploiting the semantic information associated to
virtual data stores and to the global virtual view.
\end{itemize}
Another important aim of the project is to assess the economic and
organisational impact of a semantically rich web of information on
SME industrial systems. In particular, we will analyse the
potential economic benefits expected by the \mbox{SEWASIE} system
and the internal and external factors needed to achieve such
benefits, taking care to study the business and organisational
changes requested to the firms by the new tool. Since different
firms exhibit different features concerning the required type of
information, available information sources, own skills,
manufacturing technologies and so on, \mbox{SEWASIE} has selected
to conduct the business user requirement analysis and test in two
very distinct sectors, namely the industrial moulding sector and
the textile and clothing.

\section{MOMIS}
\label{s1:Momis}

\subsection{Introduction}

In the last years the need to access distributed information and
the problem of the integration of data coming from heterogeneous
sources have become more and more important. Companies have
equipped themselves with data storing systems building up
informative systems containing data which are related one another,
but which are often redundant, heterogeneous and not always
substantial. On the other hand, the web explosion, both at
internet and intranet level, has enlarged the need for the sharing
and retrieving of information located in different sources thus
obtaining an integrated view so as to eliminate any contradiction
or redundancy. The problems that have to be faced in this field
are mainly due to both structural and application heterogeneity,
as well as to the lack of a common ontology, causing semantic
differences between information sources. Moreover these semantic
differences can cause different kinds of conflicts, ranging from
simple contradictions in names’ use (when different names are used
by different source to indicate the same concept), to structural
conflicts (when different models/primitives are used to represent
the same information). The integration problem is relevant also in
the E- Commerce environment. Electronic catalogs are a key
component of E-Commerce and they can be organized as individual
company catalogs or they can participate in a multicatalog
framework. In the second case, from a user point of view, it is
very important to have a uniform interface to search products,
that is a uniform view of data coming from different companies
catalogs and a unique query language. On the other hand, from a
company point of view it is important to guarantee both the
uniqueness of their catalogs and the participation in a
multi-catalog framework. Virtual Catalogs, as proposed in
\cite{Keller95}, synthesize this approach as they are conceived as
instruments to dynamically retrieve information from multiple
catalogs and present product data in a unified manner, without
directly storing product data from catalogs. Customers, instead of
having to interact with multiple heterogeneous catalogs, can
interact in a uniform way with a virtual catalog.

%[SI-Designer: a tool for intelligent integration of information.
%hicss2001.pdf]

In this context The problem to face is the identification of
semantically related information, that is, information describing
the same real-world concepts in different sources having semantic
heterogeneity. In fact, information sources to be integrated are
usually preexisting and have been developed independently.
Consequently, semantic heterogeneity can arise for the aspects
related to terminology, structure, and context of the information,
and has to be properly dealt with during integration in order to
effectively and correctly exploit the information available at the
sources.

\textbf{MOMIS}\footnote{http://www.dbgroup.unimo.it/Momis/} stands
for ``Mediator envirOnment for Multiple Information Sources''.
\mbox{MOMIS} is a mediator-based system for information extraction
and integration that works with structured and semistructured data
sources \cite{bergamaschi:vldb2000}. \mbox{MOMIS} was developed as
a joint collaboration among the University of Modena and Reggio
Emilia, the University of Milano and the University of Brescia,
within the INTERDATA national research project, under the
direction of Professor S. Bergamaschi. Now the research activity
continues within the \emph{D2I: Integration, Warehousing, and
Mining of Heterogeneous Data Sources} national research project.

% Corni P.h.D. Thesis

\subsection{The project}
\par The goal of information extraction and integration techniques
developed in MOMIS is to construct synthesized, integrated
descriptions (i.e., a global virtual view) of the information
coming from multiple heterogeneous sources, to provide the user
with a uniform query interface against the sources independent
from their location and the level of heterogeneity of their data.
Moreover, to meet the requirements of global, Internet-based
information systems with a possibly high number of sources to be
integrated, it is important to develop tool-assisted techniques in
order to automate as much as possible information extraction and
integration activities. This goal has been achieved with the MOMIS
project by developing the SI-Designer \cite{bergamaschi:vldb2000}.
MOMIS follows a ``semantic approach'' to information integration,
based on the building of a conceptual schemas of the information
sources, and on a mediator component. MOMIS implements a \iii
\cite{ARPA} architecture for integration and query optimization,
the mediator component relies on several tools, namely ARTEMIS
\cite{KR1998:Calvanese:Lenzerini}, developed by the University of
Milano and the University of Brescia, and ODB-Tools
\cite{ICDE_97:odbTools}, developed by the University of Modena and
Reggio Emilia and WordNet \cite{wordnet1} developed by the
Cognitive Science Laboratory at The Princeton University.


Most of the information for integration is provided by the
\emph{wrappers}, using \lang from the source description and
stored in a \lang data structure. By exploiting the WordNet
Lexical system \cite{wordnet2} and the OLCD Description Logic
inference capabilities a Common Thesaurus is built. The Common
Thesaurus is composed of relationships between schema elements
extracted from the schemata descriptions, derived from the lexical
relationships between the concepts in \mbox{WordNet}, and
explicitly stated by the integration designer. Based on the
relationships in the Common Thesaurus, affinity coefficients are
computed. These coefficients give a measure of the level of
matching between the elements of the different sources.
\mbox{MOMIS} computes a set of candidates \emph{Global Classes} by
applying affinity-based clustering on the affinity coefficients.
For each \emph{Global Class Mapping rules} are defined that
describe which \emph{local classes} belong to the global class and
how local attributes are mapped into the global attributes.

\par Part of the project is the development of a prototype which is
the software application based on the \mbox{MOMIS} theory. The
prototype is written in Java, is based on the \mbox{CORBA}
architecture and supports most of the \mbox{MOMIS} features like
the Description Logics capabilities, the ARTEMIS affinity
computation and clustering and the interaction with WordNet.


%[SI-Designer: a tool for intelligent integration of information.
%hicss2001.pdf]

\par \emph{SI-Designer} is a designer support tool for semi-automatic
integration of heterogeneous sources schema (relational, object
and semi-structured sources); it has been implemented within the
MOMIS project and it carries out integration following a semantic
approach which uses intelligent OLCD Description logics- based
techniques, clustering techniques and an ODL-ODMG extended
language to represent extracted and integrated information, \lang.
Starting from the \lang descriptions (local schema) of the source,
SI-Designer supports the designer in the creation of an integrated
view of all the sources (global schema) which is expressed in the
same \lang language. The global schema is obtained using different
stages, creating a \emph{Common Thesaurus} of intra and
inter-schema relationships. The sources to be integrated are
described through the \lang language and, by using OLCD inference
techniques, intra-schema relationships are extracted and shared in
the \emph{Common Thesaurus}. After this initial phase the
\emph{Common Thesaurus} is enriched with inter-schema
relationships, obtained in the following way: (1) using the
lexical WordNet system, which identifies the affinities between
inter-schema concepts on the basis of their denominations'
lexicon/meaning; (2) using the \mbox{ARTEMIS} \cite{er97} system,
which evaluates structural affinities among inter-schema concepts.
Starting from obtained \emph{Common Thesaurus} and using OLCD
inference and ARTEMIS clustering techniques, a global schema
containing a general view of the integrated sources is obtained.

%[SI-Designer: a tool for intelligent integration of information.
%hicss2001.pdf]

\subsection{Architecture}
The MOMIS system is designed for schema integration. \mbox{Figure
\ref{MPA}} shows the architecture of the system in terms of
functional modules. The system is composed by several object that
communicates using the CORBA\footnote{Object Management Group.
http://www.omg.org/} standard and it follows the \iii architecture
\cite{ARPA}.
\begin{figure}[ht]
  \begin{center}
    \epsfig{figure=momisarchitecture.eps,width=12cm,angle=0}
    \caption{\textbf{MOMIS} prototype architecture}\label{MPA}
  \end{center}
\end{figure}
The modules are on three levels:
\begin{description}
    \item[Data Level.] Here there are the \textbf{Wrappers}. They
are placed over each sources, represent the interface modules
between the mediator and the local data sources. Their function is
double: in the integration phase, they translate the description
of the information held in the source. This description is
supplied through the \lang{} language; in the query processing
phase, they translate the query which has been received by the
mediator (expressed in the common query \langQ{} language, derived
by the OQL language) in a query expressed in the source query
language. The wrappers must also export query result data,
providing them to the mediator through the data common model used
by the system.
    \item[Mediator Level.] The mediator is the core module of the
system. It allows to create an homogeneous representation of the
information and an integrated access to the sources. It's composed
of two distinct modules:
    \begin{itemize}
        \item The Global Schema Builder (GSB): its goal is to lead
        the designer from the schemata acquisition of sources to the
        tuning of the mapping table through the various steps of the
        integration. This module interacts with \mbox{ODB-Tools}
        (a framework for object-oriented database schema validation,
        preserving taxonomy coherence and performing taxonomic
        inference, and semantic query optimization) and with the
        lexical database WordNet to extract lexicon derived
        intensional relationships between attributes and
        classes.\\SI-Designer is the GUI (Graphic User Interface)
        for the Global Schema Builder.
        \item The Query Manager (QM): is the module that performs
        query processing and optimization. The QM generates \langQ
        queries to be sent to wrappers starting from each query
        posed by the user on the Global Schema. It automatically
        generates the translation of the query into a corresponding
        set of sub-queries for the sources and synthesize a
        unified global answer for the user.
    \end{itemize}
    \item[User Level.] The designer interacts with the Global
    Schema Builder and builds the integrated view of the sources.
    The users pose the queries on the Global Schema then the QM
    performs the queries on the local sources and present the
    users with an unified answer.
\end{description}

The original contribution of \mbox{MOMIS} is related to the
availability of a set of techniques for the designer to face
common problems that arise when integrating pre-existing
information sources, containing both semistructured and structured
data. \mbox{MOMIS} provides the capability of explicitly
introducing many kinds of knowledge for integration, such as
integrity constraints, intra- and inter-source intensional and
extensional relationships, and designer supplied domain knowledge.
A \emph{Common Thesaurus}, which has the role of a shared ontology
of the sources, is built in a semiautomatic way. The \emph{Common
Thesaurus} is a set of intra and inter-schema intensional and
extensional relationships, describing inter-schema knowledge about
classes and attributes of sources schemas; it provides a reference
on which to base the identification of classes candidate to
integration and subsequent derivation of their global
representation. \mbox{MOMIS} supports information integration in
the creation of an integrated view of all sources (Global Virtual
View) in a way automated as much as possible and performs revision
and validation of the various kinds of knowledge used for the
integration. To this end, \mbox{MOMIS} combines reasoning
capabilities of Description Logics with affinity-based clustering
techniques, by exploiting a common ontology for the sources
constructed using lexical knowledge from \mbox{WordNet} and
validated integration knowledge. The Global Virtual View is
expressed by using XML standard, to guarantee the interoperability
with other open integration system prototype.

Momis extracts the intraschema relationships by using inference
techniques, and then shares these relationships in the Common
Thesaurus. After this initial phase, Momis enriches the common
thesaurus with interschema relationships obtained using the
lexical WordNet\footnote{http://www.cogsci.princeton.edu/wn}
system, which identifies the affinities between interschema
concepts on the basis of their lexicon meaning. Momis also
enriches the Common Thesaurus using the \mbox{ARTEMIS} system
\cite{IEEE2002}, which evaluates structural affinities among
interschema concepts. All information about integration are saved
in the Global Schema which is the main CORBA object to access the
MOMIS integration services.


%%% Local Variables:
%%% mode: latex
%%% TeX-master: "tesi"
%%% End:
