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Knowledge representation has always been a central issue for AI, and as
a sub-discipline within Computer Science it's primary contribution is
probably the beginnings of a computational theory of knowledge. While it
is still too early to speak of such a theory, some key aspects of good
knowledge representation are becoming clear [REF438] .
The text captured in document
corpora was not entered with the { intention} of being part of a
knowledge base. These are documents written by someone as part of a
natural communication process, and any search engine technology simply
gives this document added life. Alternatively, we can say that the
document {\em was} intended to become part of a ``knowledge base,'' but
one that pre-dates (at least the AI) use of that term: people publish
their documents with the explicit hope that their ideas can become part
of our collective wisdom and used by others.
Note the ease with which
author-as-knowledge engineer can express their knowledge. Hypertext
knowledge bases are accessible to every writer. In this view, hypertext
solves the key AI problem of the KNOWLEDGE ACQUISITION BOTTLENECK
, providing a knowledge representation language with the ease,
flexibility and expressiveness of natural language --- by actually using
natural language! The cost paid is the weakness of the inferences that
can be made from a textual foundation: contrast the strong
theorem-proving notions of inference of Section §6.5.1 with the many confounded
associations which arise in Swanson's analysis of latent knowledge in
Section §6.5.3 .
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Text-based intelligence
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