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Books > Computing & IT > Applications of computing > Databases
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Information Retrieval: Uncertainty and Logics - Advanced Models for the Representation and Retrieval of Information (Hardcover, 1998 ed.)
Loot Price: R8,580
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Information Retrieval: Uncertainty and Logics - Advanced Models for the Representation and Retrieval of Information (Hardcover, 1998 ed.)
Series: The Information Retrieval Series, 4
Expected to ship within 12 - 17 working days
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In recent years, there have been several attempts to define a logic
for information retrieval (IR). The aim was to provide a rich and
uniform representation of information and its semantics with the
goal of improving retrieval effectiveness. The basis of a logical
model for IR is the assumption that queries and documents can be
represented effectively by logical formulae. To retrieve a
document, an IR system has to infer the formula representing the
query from the formula representing the document. This logical
interpretation of query and document emphasizes that relevance in
IR is an inference process. The use of logic to build IR models
enables one to obtain models that are more general than earlier
well-known IR models. Indeed, some logical models are able to
represent within a uniform framework various features of IR systems
such as hypermedia links, multimedia data, and user's knowledge.
Logic also provides a common approach to the integration of IR
systems with logical database systems. Finally, logic makes it
possible to reason about an IR model and its properties. This
latter possibility is becoming increasingly more important since
conventional evaluation methods, although good indicators of the
effectiveness of IR systems, often give results which cannot be
predicted, or for that matter satisfactorily explained. However,
logic by itself cannot fully model IR. The success or the failure
of the inference of the query formula from the document formula is
not enough to model relevance in IR. It is necessary to take into
account the uncertainty inherent in such an inference process. In
1986, Van Rijsbergen proposed the uncertainty logical principle to
model relevance as an uncertain inference process. When proposing
the principle, Van Rijsbergen was not specific about which logic
and which uncertainty theory to use. As a consequence, various
logics and uncertainty theories have been proposed and
investigated. The choice of an appropriate logic and uncertainty
mechanism has been a main research theme in logical IR modeling
leading to a number of logical IR models over the years.
Information Retrieval: Uncertainty and Logics contains a collection
of exciting papers proposing, developing and implementing logical
IR models. This book is appropriate for use as a text for a
graduate-level course on Information Retrieval or Database Systems,
and as a reference for researchers and practitioners in industry.
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