Abstract
Existing search engines––with Google at the top––have many remarkable capabilities; but what is not
among them is deduction capability––the capability to synthesize an answer to a query from bodies of
information which reside in various parts of the knowledge base.
In recent years, impressive progress has been made in enhancing performance of search engines through
the use of methods based on bivalent logic and bivalent-logic-based probability theory. But can such
methods be used to add nontrivial deduction capability to search engines, that is, to upgrade search engines
to question-answering systems? A view which is articulated in this note is that the answer is ‘‘No.’’ The
problem is rooted in the nature of world knowledge, the kind of knowledge that humans acquire through
experience and education.
It is widely recognized that world knowledge plays an essential role in assessment of relevance, summarization,
search and deduction. But a basic issue which is not addressed is that much of world knowledge
is perception-based, e.g., ‘‘it is hard to find parking in Paris,’’ ‘‘most professors are not rich,’’ and ‘‘it is
unlikely to rain in midsummer in San Francisco.’’ The problem is that (a) perception-based information is
intrinsically fuzzy; and (b) bivalent logic is intrinsically unsuited to deal with fuzziness and partial truth.
To come to grips with fuzziness of world knowledge, new tools are needed. The principal new tool––a
tool which is briefly described in this note––is Precisiated Natural Language (PNL). PNL is based on fuzzy
logic and has the capability to deal with partiality of certainty, partiality of possibility and partiality of
truth. These are the capabilities that are needed to be able to draw on world knowledge for assessment of
relevance, and for summarization, search and deduction.
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