Annotating the semantic web

The web of today has evolved into a huge repository of rich Multimedia content
for human consumption. The exponential growth of the web made it possible for
information size to reach astronomical proportions; far more than a mere human can
manage, causing the problem of information overload. Because of this, the creators of
the web(lO) spoke of using computer agents in order to process the large amounts of
data. To do this, they planned to extend the current web to make it understandable
by computer programs. This new web is being referred to as the Semantic Web.
Given the huge size of the web, a collective effort is necessary to extend the web. For
this to happen, tools easy enough for non-experts to use must be available.
This thesis first proposes a methodology which semi-automatically labels semantic
entities in web pages. The methodology first requires a user to provide some initial
examples. The tool then learns how to reproduce the user's examples and generalises
over them by making use of Adaptive Information Extraction (AlE) techniques. When
its level of performance is good enough when compared to the user, it then takes over
the process and processes the remaining documents autonomously.
The second methodology goes a step further and attempts to gather semantically
typed information from web pages automatically. It starts from the assumption that
semantics are already available all over the web, and by making use of a number of
freely available resources (like databases) combined with AlE techniques, it is possible
to extract most information automatically.
These techniques will certainly not provide all the solutions for the problems brought about with the advent of the Semantic Web. They are intended to provide
a step forward towards making the Semantic Web a reality.