The Semantic Web - Social Semantics: The Search for Meaning on the Web - page 81

The difference of the project is one both of scope and goal. The Semantic Web is,
at first glance at least, a more modest project than artificial intelligence. To review
the claims of artificial intelligence in order to clarify their relation to the Semantic
Web, we are best served by remembering the goal of AI as stated by John McCarthy
at the 1956 Dartmouth Conference, “the study is to proceed on the basis of the
conjecture that every aspect of learning or any other feature of intelligence can
in principle be so precisely described that a machine can be made to simulate it”
(McCarthy et al. 1955). However, 'intelligence' itself is not even vaguely defined.
The proposal put forward by McCarthy gave a central role to “common-sense,” so
that “a program has common sense if it automatically deduces for itself a sufficient
wide class of immediate consequences of anything it is told and what it already
knows” (1959).
In contrast, the Semantic Web does not seek to replicate human intelligence and
encode all common-sense knowledge in some universal representational scheme.
The Semantic Web instead leaves “aside the artificial intelligence problem of
training machines to behave like people” but instead tries to develop a representation
language that can complement human intelligence, for “the Web was designed as
an information space, with the goal that it should be useful not only for human-
human communication, but also that machines would be able to participate and
help” (Berners-Lee 1998c). Despite appearances, the Semantic Web is in the spirit
of Licklider and Engelbart rather than McCarthy, Minsky, and even latter-day
proponents of AI like Brooks. Berners-Lee is explicit that the project of encoding
human intelligence is not part of the problem, as the Semantic Web “does not
imply some magical artificial intelligence which allows machines to comprehend
human mumblings. It only indicates a machine's ability to solve a well-defined
problem by performing well-defined operations on existing well-defined data”
(Berners-Lee 1998c). Instead, the Semantic Web is an intellectual project whose
goal is philosophically the opposite of artificial intelligence, the creation of new
forms of collective intelligence. As phrased by Licklider, this would be a “man-
machine symbiosis,” in which in “the anticipated symbiotic partnership, men will
set the goals, formulate the hypotheses, determine the criteria, and perform the
evaluations. Computing machines will do the routinizable work that must be done
to prepare the way for insights and decisions” (1960).
While the goals of the Semantic Web are different, it does still employ the same
fundamental technology as classical artificial intelligence: knowledge representa-
tion languages. As put by Berners-Lee, “The Semantic Web is what we will get
if we perform the same globalization process to knowledge representation that the
Web initially did to hypertext” (Berners-Lee 1998c). Yet there is a question about
whether or not knowledge representation itself might be the problem, not just scale.
As put by Karen Sparck Jones, one of the founders of information retrieval, “there
are serious problems about the core [Semantic Web] idea of combining substantive
formal description with world-wide reach, i.e. having your cake and eating it, even
if the cake is only envisaged as more like a modest sponge cake than the rich fruit
cake that AI would like to have” (2004). So the problem may lie in the very use of
knowledge representation language itself. So far we have shown that the properties