Il metodo Sewcom
non è che un tentativo di preparare la scuola e
gli ambienti educativi a quello che viene definito "Semantic
Web": la possibilità cioè di accedere
ad un universo informativo organizzato e consistente,
dove operano gli "agenti intelligenti" come
supporto alla ricerca di informazioni e alla costruzione
di conoscenza.
Il Semantic Web è la visione di Tim Berners Lee,
l'"inventore" dell'attuale Web.
Ecco alcuni indirizzi dove cominciare l'esplorazione di
questa affascinante prospettiva:
The
Semantic Web
A new form of Web content that is meaningful to computers
will unleash a revolution of new possibilities
by TIM BERNERS-LEE, JAMES HENDLER and ORA
LASSILA
FURTHER INFORMATION
The entertainment system was belting out the Beatles'
"We Can Work It Out" when the phone rang. When
Pete answered, his phone turned the sound down by sending
a message to all the other local devices that had a volume
control. His sister, Lucy, was on the line from the doctor's
office: "Mom needs to see a specialist and then has
to have a series of physical therapy sessions. Biweekly
or something. I'm going to have my agent set up the appointments."
Pete immediately agreed to share the chauffeuring.
At the doctor's office, Lucy instructed her Semantic Web
agent through her handheld Web browser. The agent promptly
retrieved information about Mom's prescribed treatment
from the doctor's agent, looked up several lists of providers,
and checked for the ones in-plan for Mom's insurance within
a 20-mile radius of her home and with a rating of excellent
or very good on trusted rating services. It then began
trying to find a match between available appointment times
(supplied by the agents of individual providers through
their Web sites) and Pete's and Lucy's busy schedules.
(The emphasized keywords indicate terms whose semantics,
or meaning, were defined for the agent through the Semantic
Web.)
In a few minutes the agent presented them
with a plan. Pete didn't like it—University Hospital
was all the way across town from Mom's place, and he'd
be driving back in the middle of rush hour. He set his
own agent to redo the search with stricter preferences
about location and time. Lucy's agent, having complete
trust in Pete's agent in the context of the present task,
automatically assisted by supplying access certificates
and shortcuts to the data it had already sorted through.
Almost instantly the new plan was presented:
a much closer clinic and earlier times—but there
were two warning notes. First, Pete would have to reschedule
a couple of his less important appointments. He checked
what they were—not a problem. The other was something
about the insurance company's list failing to include
this provider under physical therapists: "Service
type and insurance plan status securely verified by other
means," the agent reassured him. "(Details?)"
Lucy registered her assent at about the
same moment Pete was muttering, "Spare me the details,"
and it was all set. (Of course, Pete couldn't resist the
details and later that night had his agent explain how
it had found that provider even though it wasn't on the
proper list.)
Expressing Meaning
Pete and Lucy could use their agents to carry out all
these tasks thanks not to the World Wide Web of today
but rather 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 adeptly parse Web pages for layout and routine
processing—here a header, there a link to another
page—but in general, computers have no reliable way
to process the semantics: this is the home page of the
Hartman and Strauss Physio Clinic, this link goes to Dr.
Hartman's curriculum vitae.
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 clinic's Web page will know not just that the page
has keywords such as "treatment, medicine, physical,
therapy" (as might be encoded today) but also that
Dr. Hartman works at this clinic on Mondays, Wednesdays
and Fridays and that the script takes a date range in
yyyy-mm-dd format and returns appointment times. And it
will "know" all this without needing artificial
intelligence on the scale of 2001's Hal or Star Wars's
C-3PO. Instead these semantics were encoded into the Web
page when the clinic's office manager (who never took
Comp Sci 101) massaged it into shape using off-the-shelf
software for writing Semantic Web pages along with resources
listed on the Physical Therapy Association's site.
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 usher in significant 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, must not discriminate between the
scribbled draft and the polished performance, 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 of the scale we have everything
from the five-second TV commercial to poetry. At the other
end we have databases, programs and sensor output. To
date, the Web has developed most rapidly as a medium of
documents for people rather than for data and information
that can be processed automatically. The Semantic Web
aims to make up for this.
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. Decentralization
requires compromises: the Web had to throw away the ideal
of total consistency of all of its interconnections, ushering
in the infamous message "Error 404: Not Found"
but allowing unchecked exponential growth.
Knowledge Representation
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, as this technology is often
called, is currently in a state comparable to that of
hypertext before the advent of the Web: it is clearly
a good idea, and some very nice demonstrations exist,
but it has not yet changed the world. It contains the
seeds of important applications, but to realize its full
potential it must be linked into a single global system.
WEB SEARCHES TODAY
Traditional knowledge-representation systems typically
have been centralized, requiring everyone to share exactly
the same definition of common concepts such as "parent"
or "vehicle." But central control is stifling,
and increasing the size and scope of such a system rapidly
becomes unmanageable.
Moreover, these systems usually carefully
limit the questions that can be asked so that the computer
can answer reliably— or answer at all. The problem
is reminiscent of Gödel's theorem from mathematics:
any system that is complex enough to be useful also encompasses
unanswerable questions, much like sophisticated versions
of the basic paradox "This sentence is false."
To avoid such problems, traditional knowledge-representation
systems generally each had their own narrow and idiosyncratic
set of rules for making inferences about their data. For
example, a genealogy system, acting on a database of family
trees, might include the rule "a wife of an uncle
is an aunt." Even if the data could be transferred
from one system to another, the rules, existing in a completely
different form, usually could not.
Semantic Web researchers, in contrast,
accept that paradoxes and unanswerable questions are a
price that must be paid to achieve versatility. We make
the language for the rules as expressive as needed to
allow the Web to reason as widely as desired. This philosophy
is similar to that of the conventional Web: early in the
Web's development, detractors pointed out that it could
never be a well-organized library; without a central database
and tree structure, one would never be sure of finding
everything. They were right. But the expressive power
of the system made vast amounts of information available,
and search engines (which would have seemed quite impractical
a decade ago) now produce remarkably complete indices
of a lot of the material out there. 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 questions—is the task before the Semantic
Web community at the moment. A mixture of mathematical
and engineering decisions complicate this task. The logic
must be powerful enough to describe complex properties
of objects but not so powerful that agents can be tricked
by being asked to consider a paradox. Fortunately, a large
majority of the information we want to express is along
the lines of "a hex-head bolt is a type of machine
bolt," which is readily written in existing languages
with a little extra vocabulary.
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 such as <zip code> or <alma mater>
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 script writer has to know what the page
writer uses each tag for. In short, XML allows users to
add arbitrary structure to their documents but says nothing
about what the structures mean [see "XML and the
Second-Generation Web," by Jon Bosak and Tim Bray;
Scientific American, May 1999].
The Semantic Web will enable machines to
COMPREHEND semantic documents and data, not human speech
and writings.
--------------------------------------------------------------------------------
Meaning is expressed by RDF, which encodes it in sets
of triples, each triple being rather like the subject,
verb and object of an elementary sentence. These triples
can be written using XML tags. In RDF, a document makes
assertions that particular things (people, Web pages or
whatever) have properties (such as "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. (URLs, Uniform Resource
Locators, are the most common type of URI.) The verbs
are also identified by URIs, which enables anyone to define
a new concept, a new verb, just by defining a URI for
it somewhere on the Web.
Human language thrives when using the same
term to mean somewhat different things, but automation
does not. Imagine that I hire a clown messenger service
to deliver balloons to my customers on their birthdays.
Unfortunately, the service transfers the addresses from
my database to its database, not knowing that the "addresses"
in mine are where bills are sent and that many of them
are post office boxes. My hired clowns end up entertaining
a number of postal workers—not necessarily a bad
thing but certainly not the intended effect. Using a different
URI for each specific concept solves that problem. An
address that is a mailing address can be distinguished
from one that is a street address, and both can be distinguished
from an address that is a speech.
The triples of RDF form webs of information
about related things. Because RDF uses URIs to encode
this information in a document, the URIs ensure that concepts
are not just words in a document but are tied to a unique
definition that everyone can find on the Web. For example,
imagine that we have access to a variety of databases
with information about people, including their addresses.
If we want to find people living in a specific zip code,
we need to know which fields in each database represent
names and which represent zip codes. RDF can specify that
"(field 5 in database A) (is a field of type) (zip
code)," using URIs rather than phrases for each term.
Ontologies
Of course, this is not the end of the story, because two
databases may use different identifiers for what is in
fact the same concept, such as zip code. A program that
wants to compare or combine information across the two
databases has to know that these two terms are being used
to mean the same thing. 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 ontologies. In philosophy, an ontology is a theory
about the nature of existence, of what types of things
exist; ontology as a discipline studies such theories.
Artificial-intelligence and Web researchers have co-opted
the term for their own jargon, and for them an ontology
is 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. If city
codes must be of type city and cities generally have Web
sites, we can discuss the Web site associated with a city
code even if no database links a city code directly to
a Web site.
Inference rules in ontologies supply further
power. An ontology may express the rule "If a city
code is associated with a state code, and an address uses
that city code, then that address has the associated state
code." A program could then readily deduce, for instance,
that a Cornell University address, being in Ithaca, must
be in New York State, which is in the U.S., and therefore
should be formatted to U.S. standards. 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. Of course,
the same problems as before now arise if I point to an
ontology that defines addresses as containing a zip code
and you point to one that uses postal code. This kind
of confusion can be resolved if ontologies (or other Web
services) provide equivalence relations: one or both of
our ontologies may contain the information that my zip
code is equivalent to your postal code.
Our scheme for sending in the clowns to
entertain my customers is partially solved when the two
databases point to different definitions of address. The
program, using distinct URIs for different concepts of
address, will not confuse them and in fact will need to
discover that the concepts are related at all. The program
could then use a service that takes a list of postal addresses
(defined in the first ontology) and converts it into a
list of physical addresses (the second ontology) by recognizing
and removing post office boxes and other unsuitable addresses.
The structure and semantics provided by ontologies make
it easier for an entrepreneur to provide such a service
and can make its use completely transparent.
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. An example of
a page marked up for such use is online at http://www.cs.umd.edu/~hendler.
If you send your Web browser to that page, you will see
the normal Web page entitled "Dr. James A. Hendler."
As a human, you can readily find the link to a short biographical
note and read there that Hendler received his Ph.D. from
Brown University. A computer program trying to find such
information, however, would have to be very complex to
guess that this information might be in a biography and
to understand the English language used there.
For computers, the page is linked to an
ontology page that defines information about computer
science departments. For instance, professors work at
universities and they generally have doctorates. Further
markup on the page (not displayed by the typical Web browser)
uses the ontology's concepts to specify that Hendler received
his Ph.D. from the entity described at the URI http://www.
brown.edu — the Web page for Brown. Computers can
also find that Hendler is a member of a particular research
project, has a particular e-mail address, and so on. All
that information is readily processed by a computer and
could be used to answer queries (such as where Dr. Hendler
received his degree) that currently would require a human
to sift through the content of various pages turned up
by a search engine.
In addition, this markup makes it much
easier to develop programs that can tackle complicated
questions whose answers do not reside on a single Web
page. Suppose you wish to find the Ms. Cook you met at
a trade conference last year. You don't remember her first
name, but you remember that she worked for one of your
clients and that her son was a student at your alma mater.
An intelligent search program can sift through all the
pages of people whose name is "Cook" (sidestepping
all the pages relating to cooks, cooking, the Cook Islands
and so forth), find the ones that mention working for
a company that's on your list of clients and follow links
to Web pages of their children to track down if any are
in school at the right place.
Agents
AGENTS
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. The Semantic Web promotes
this synergy: even agents that were not expressly designed
to work together can transfer data among themselves when
the data come with semantics.
An important facet of agents' functioning will be the
exchange of "proofs" written in the Semantic
Web's unifying language (the language that expresses logical
inferences made using rules and information such as those
specified by ontologies). For example, suppose Ms. Cook's
contact information has been located by an online service,
and to your great surprise it places her in Johannesburg.
Naturally, you want to check this, so your computer asks
the service for a proof of its answer, which it promptly
provides by translating its internal reasoning into the
Semantic Web's unifying language. An inference engine
in your computer readily verifies that this Ms. Cook indeed
matches the one you were seeking, and it can show you
the relevant Web pages if you still have doubts. Although
they are still far from plumbing the depths of the Semantic
Web's potential, some programs can already exchange proofs
in this way, using the current preliminary versions of
the unifying language.
Another vital feature will be digital signatures,
which are encrypted blocks of data that computers and
agents can use to verify that the attached information
has been provided by a specific trusted source. You want
to be quite sure that a statement sent to your accounting
program that you owe money to an online retailer is not
a forgery generated by the computer-savvy teenager next
door. Agents should be skeptical of assertions that they
read on the Semantic Web until they have checked the sources
of information. (We wish more people would learn to do
this on the Web as it is!)
Many automated Web-based services already
exist without semantics, but other programs such as agents
have no way to locate one that will perform a specific
function. This process, called service discovery, can
happen only when there is a common language to describe
a service in a way that lets other agents "understand"
both the function offered and how to take advantage of
it. Services and agents can advertise their function by,
for example, depositing such descriptions in directories
analogous to the Yellow Pages.
Some low-level service-discovery schemes
are currently available, such as Microsoft's Universal
Plug and Play, which focuses on connecting different types
of devices, and Sun Microsystems's Jini, which aims to
connect services. These initiatives, however, attack the
problem at a structural or syntactic level and rely heavily
on standardization of a predetermined set of functionality
descriptions. Standardization can only go so far, because
we can't anticipate all possible future needs.
Properly designed, the Semantic Web can
assist the evolution of human knowledge as a whole.
--------------------------------------------------------------------------------
The Semantic Web, in contrast, is more flexible. The consumer
and producer agents can reach a shared understanding by
exchanging ontologies, which provide the vocabulary needed
for discussion. Agents can even "bootstrap"
new reasoning capabilities when they discover new ontologies.
Semantics also makes it easier to take advantage of a
service that only partially matches a request.
A typical process will involve the creation
of a "value chain" in which subassemblies of
information are passed from one agent to another, each
one "adding value," to construct the final product
requested by the end user. Make no mistake: to create
complicated value chains automatically on demand, some
agents will exploit artificial-intelligence technologies
in addition to the Semantic Web. But the Semantic Web
will provide the foundations and the framework to make
such technologies more feasible.
Putting all these features together results
in the abilities exhibited by Pete's and Lucy's agents
in the scenario that opened this article. Their agents
would have delegated the task in piecemeal fashion to
other services and agents discovered through service advertisements.
For example, they could have used a trusted service to
take a list of providers and determine which of them are
in-plan for a specified insurance plan and course of treatment.
The list of providers would have been supplied by another
search service, et cetera. These activities formed chains
in which a large amount of data distributed across the
Web (and almost worthless in that form) was progressively
reduced to the small amount of data of high value to Pete
and Lucy—a plan of appointments to fit their schedules
and other requirements.
In the next step, the Semantic Web will
break out of the virtual realm and extend into our physical
world. URIs can point to anything, including physical
entities, which means we can use the RDF language to describe
devices such as cell phones and TVs. Such devices can
advertise their functionality—what they can do and
how they are controlled—much like software agents.
Being much more flexible than low-level schemes such as
Universal Plug and Play, such a semantic approach opens
up a world of exciting possibilities.
For instance, what today is called home
automation requires careful configuration for appliances
to work together. Semantic descriptions of device capabilities
and functionality will let us achieve such automation
with minimal human intervention. A trivial example occurs
when Pete answers his phone and the stereo sound is turned
down. Instead of having to program each specific appliance,
he could program such a function once and for all to cover
every local device that advertises having a volume control
— the TV, the DVD player and even the media players
on the laptop that he brought home from work this one
evening.
The first concrete steps have already been
taken in this area, with work on developing a standard
for describing functional capabilities of devices (such
as screen sizes) and user preferences. Built on RDF, this
standard is called Composite Capability/Preference Profile
(CC/PP). Initially it will let cell phones and other nonstandard
Web clients describe their characteristics so that Web
content can be tailored for them on the fly. Later, when
we add the full versatility of languages for handling
ontologies and logic, devices could automatically seek
out and employ services and other devices for added information
or functionality. It is not hard to imagine your Web-enabled
microwave oven consulting the frozen-food manufacturer's
Web site for optimal cooking parameters.
Evolution of Knowledge
ELABORATE, PRECISE
SEARCHES
The semantic web is not "merely" the tool for
conducting individual tasks that we have discussed so
far. In addition, if properly designed, the Semantic Web
can assist the evolution of human knowledge as a whole.
Human endeavor is caught in an eternal tension between
the effectiveness of small groups acting independently
and the need to mesh with the wider community. A small
group can innovate rapidly and efficiently, but this produces
a subculture whose concepts are not understood by others.
Coordinating actions across a large group, however, is
painfully slow and takes an enormous amount of communication.
The world works across the spectrum between these extremes,
with a tendency to start small—from the personal
idea—and move toward a wider understanding over time.
An essential process is the joining together
of subcultures when a wider common language is needed.
Often two groups independently develop very similar concepts,
and describing the relation between them brings great
benefits. Like a Finnish-English dictionary, or a weights-and-measures
conversion table, the relations allow communication and
collaboration even when the commonality of concept has
not (yet) led to a commonality of terms.
The Semantic Web, in naming every concept
simply by a URI, lets anyone express new concepts that
they invent with minimal effort. Its unifying logical
language will enable these concepts to be progressively
linked into a universal Web. This structure will open
up the knowledge and workings of humankind to meaningful
analysis by software agents, providing a new class of
tools by which we can live, work and learn together.
Weaving the Web: The Original Design and
Ultimate Destiny of the World Wide Web by Its Inventor.
Tim Berners-Lee, with Mark Fischetti. Harper San Francisco,
1999.
An enhanced version of this article is on the Scientific
American Web site, with additional material and links.
World Wide Web Consortium (W3C): www.w3.org/
W3C Semantic Web Activity: www.w3.org/2001/sw/
An introduction to ontologies: www.SemanticWeb.org/knowmarkup.html
Simple HTML Ontology Extensions Frequently
Asked Questions (SHOE FAQ): www.cs.umd.edu/projects/plus/SHOE/faq.html
DARPA Agent Markup Language (DAML) home
page: www.daml.org/
TIM BERNERS-LEE, JAMES HENDLER and ORA LASSILA are individually
and collectively obsessed with the potential of Semantic
Web technology. Berners-Lee is director of the World Wide
Web Consortium (W3C) and a researcher at the Laboratory
for Computer Science at the Massachusetts Institute of
Technology. When he invented the Web in 1989, he intended
it to carry more semantics than became common practice.
Hendler is professor of computer science at the University
of Maryland at College Park, where he has been doing research
on knowledge representation in a Web context for a number
of years. He and his graduate research group developed
SHOE, the first Web-based knowledge representation language
to demonstrate many of the agent capabilities described
in this article. Hendler is also responsible for agent-based
computing research at the Defense Advanced Research Projects
Agency (DARPA) in Arlington, Va. Lassila is a research
fellow at the Nokia Research Center in Boston, chief scientist
of Nokia Venture Partners and a member of the W3C Advisory
Board. Frustrated with the difficulty of building agents
and automating tasks on the Web, he co-authored W3C’s
RDF specification, which serves as the foundation for
many current Semantic Web efforts.
