The Semantic Web (or Web 3.0) promises to “organize the world’s
information” in a dramatically more logical way than Google can ever
achieve with their current engine design. This is specially true from
the point of view of machine comprehension as opposed to human
comprehension.The Semantic Web requires the use of a declarative
ontological language like OWL to produce domain-specific ontologies
that machines can use to reason about information and make new
conclusions, not simply match keywords.
However, the Semantic Web, which is still in a development phase
where researchers are trying to define the best and most usable design
models, would require the participation of thousands of knowledgeable
people over time to produce those domain-specific ontologies necessary
for its functioning.
Machines (or machine-based reasoning, aka AI software or ‘info
agents’) would then be able to use those laboriously –but not entirely
manually– constructed ontologies to build a view (or formal model) of
how the individual terms within the information relate to each other.
Those relationships can be thought of as the axioms (basic
assumptions), which together with the rules governing the inference
process both enable as well as constrain the interpretation (and well-formed use)
of those terms by the info agents to reason new conclusions based on
existing information, i.e. to think. In other words, theorems (formal
deductive propositions that are provable based on the axioms and the
rules of inference) may be generated by the software, thus allowing
formal deductive reasoning at the machine level. And given that an
ontology, as described here, is a statement of Logic Theory, two or
more independent info agents processing the same domain-specific
ontology will be able to collaborate and deduce an answer to a query,
without being driven by the same software.
Thus, and as stated, in the Semantic Web individual machine-based
agents (or a collaborating group of agents) will be able to understand
and use information by translating concepts and deducing new
information rather than just matching keywords.
Once machines can understand and use information,
using a standard ontology language, the world will never be the same.
It will be possible to have an info agent (or many info agents) among
your virtual AI-enhanced workforce each having access to different
domain specific comprehension space and all communicating with each
other to build a collective consciousness.
You’ll be able to ask your info agent or agents to find you the
nearest restaurant that serves Italian cuisine, even if the restaurant
nearest you advertises itself as a Pizza joint as opposed to an Italian
restaurant. But that is just a very simple example of the deductive
reasoning machines will be able to perform on information they have.
Far more awesome implications can be seen when you consider that
every area of human knowledge will be automatically within the
comprehension space of your info agents. That is because each info
agent can communicate with other info agents who are specialized in
different domains of knowledge to produce a collective consciousness
(using the Borg metaphor) that encompasses all human knowledge. The
collective “mind” of those agents-as-the-Borg will be the Ultimate
Answer Machine, easily displacing Google from this position, which it
does not truly fulfill.
The problem with the Semantic Web, besides that researchers are
still debating which design and implementation of the ontology language
model (and associated technologies) is the best and most usable, is
that it would take thousands or tens of thousands of knowledgeable
people many years to boil down human knowledge to domain specific
ontologies.
However, if we were at some point to take the Wikipedia community and give them the right
tools and standards to work with (whether existing or to be developed
in the future), which would make it possible for reasonably skilled
individuals to help reduce human knowledge to domain-specific
ontologies, then that time can be shortened to just a few years, and
possibly to as little as two years.
The emergence of a Wikipedia 3.0 (as in Web 3.0, aka Semantic Web)
that is built on the Semantic Web model will herald the end of Google
as the Ultimate Answer Machine. It will be replaced with “WikiMind”
which will not be a mere search engine like Google is but a true Global
Brain: a powerful pan-domain inference engine, with a vast set of
ontologies (a la Wikipedia 3.0) covering all domains of human
knowledge, that can reason and deduce answers instead of just throwing
raw information at you using the outdated concept of a search engine.
Notes
After writing the original post I found out that the Wikipedia
application, also known as MediaWiki and not to be confused with
Wikipedia.org, has already been used to implement ontologies. The name that they’ve chosen is Ontoworld. I think WikiMind or WikiBorg would have been a cooler name, but I like ontoworld, too, as in “and it descended onto the world,” since that may be a reference to the global mind a Semantic-Web-enabled OntoWorld would lead to.
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