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.
