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Before
discussing the specific research goals, the globalcontext of the research is
discussed using a case study.
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A travel agent
in Antwerp has a client who wants to go to St.Tropez in France. There are
rather a lot of possibilities for composing such a voyage. The client can
take the train to France, or he can take a bus or train to Brussels and then
the airplane to Nice in France, or the train to Paris then the airplane or
another train to Nice. The travel agent explains the client that there are a
lot of possibilities. During his explanation he gets an impression of what
the client really wants.
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The travel agent
agrees with the client about the itinerary: by train from Antwerp to
Brussels, by airplane from Brussels to Nice and by train from Nice to St.
Tropez. This still leaves room for some alternatives. The client will come
back to make a final decision once the travel agent has said him by mail that
he has worked out some alternative solutions like price for first class vs
second class etc...
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Remark that the
decision for the itinerary that has been taken is not very well founded; only
very crude price comparisons have been done based on some internet sites that
the travel agent consulted during his conversation with the client. A very
cheap flight from Antwerp to Cannes has escaped the attention of the travel
agent.
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The travel agent
will now further consult the internet sites of the Belgium railways, the
Brussels airport and the France railways to get some alternative prices,
departure times and total travel times.
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Now let’s compare this with the
hypothetical situation that a full blown Semantic Web would exist. In the
computer of the travel agent resides a Semantic Web agent that has at its
disposal all the necessary standard tools. The travel agent has a specialised
interface to the general Semantic Web agent. He fills in a query in his
specialised screen. This query is translated to a standardised query format
for the Semantic Web agent. The agent consult his rule database. This
database of course contains a lot of rules about travelling as well as facts
like e.g. facts about internet sites where information can be obtained. There
are a lot of ‘path’ rules: rules for composing an itinerary. The agent contacts different other agents
like the agent of the Belgium railways, the agents of the French railways,
the agent of the airports of Antwerp, Brussels, Paris, Cannes, Nice etc...
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With the
information received its inference rules about scheduling a trip are
consulted. This is all done while the travel agent is chatting with the
client to detect his preferences. After some 5 minutes the Semantic Web agent
gives the travel agent a list of alternatives for the trip; now the travel
agent can immediately discuss this with his client. When a decision has been
reached, the travel agent immediately gives his Semantic Web agent the order
for making the reservations and ordering the tickets. Now the client only
will have to come back once for getting his tickets and not twice. The travel
agent not only has been able to propose a cheaper trip as in the case above
but has also gained an important amount of time.
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