Sanskrit & Artificial Intelligence — NASA
Knowledge Representation in Sanskrit and Artificial Intelligence
By Rick Briggs
Roacs, NASA Ames Research Center, Moffet Field, California
Abstract
In the past twenty years, much time, effort, and money
has been expended on designing an unambiguous
representation of natural languages to make them
accessible to computer processing. These efforts have
centered around creating schemata designed to parallel
logical relations with relations expressed by the syntax
and semantics of natural languages, which are clearly
cumbersome and ambiguous in their function as vehicles
for the transmission of logical data. Understandably,
there is a widespread belief that natural languages are
unsuitable for the transmission of many ideas that
artificial languages can render with great precision and
mathematical rigor.
But this dichotomy, which has served as a premise
underlying much work in the areas of linguistics and
artificial intelligence, is a false one. There is at
least one language, Sanskrit, which for the duration of
almost 1,000 years was a living spoken language with a
considerable literature of its own. Besides works of
literary value, there was a long philosophical and
grammatical tradition that has continued to exist with
undiminished vigor until the present century. Among the
accomplishments of the grammarians can be reckoned a
method for paraphrasing Sanskrit in a manner that is
identical not only in essence but in form with current
work in Artificial Intelligence. This article
demonstrates that a natural language can serve as an
artificial language also, and that much work in AI has
been reinventing a wheel millenia old.
First, a typical Knowledge Representation Scheme (using
Semantic Nets) will be laid out, followed by an outline
of the method used by the ancient Indian Grammarians to
analyze sentences unambiguously. Finally, the clear
parallelism between the two will be demonstrated, and the
theoretical implications of this equivalence will be
given.
Semantic Nets
For the sake of comparison, a brief overview of semantic
nets will be given, and examples will be included that
will be compared to the Indian approach. After early
attempts at machine translation (which were based to a
large extent on simple dictionary look-up) failed in
their effort to teach a computer to understand natural
language, work in AI turned to Knowledge Representation.
Since translation is not simply a map from lexical item
to lexical item, and since ambiguity is inherent in a
large number of utterances, some means is required to
encode what the actual meaning of a sentence is. Clearly,
there must be a representation of meaning independent of
words used. Another problem is the interference of
syntax. In some sentences (for example active/passive)
syntax is, for all intents and purposes, independent of
meaning. Here one would like to eliminate considerations
of syntax. In other sentences the syntax contributes to
the meaning and here one wishes to extract it.
Continues at:
http://www.vedicsciences.net/articles/sanskrit-nasa.html
Jai Maharaj, Jyotishi
Om Shanti
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