Information retrieval used to mean looking through thousands of strings of texts to find words
or symbols that matched a user's query. Today there are many models that help index and search
more effectively so retrieval takes a lot less time. Information retrieval (IR) is often seen
as a subfield of computer science and shares some modeling applications storage applications
and techniques as do other disciplines like artificial intelligence database management and
parallel computing. This book introduces the topic of IR and how it differs from other computer
science disciplines. A discussion of the history of modern IR is briefly presented and the
notation of IR as used in this book is defined. The complex notation of relevance is discussed.
Some applications of IR is noted as well since IR has many practical uses today. Using
information retrieval with fuzzy logic to search for software terms can help find software
components and ultimately help increase the reuse of software. This is just one practical
application of IR that is covered in this book. Some of the classical models of IR is presented
as a contrast to extending the Boolean model. This includes a brief mention of the source of
weights for the various models. In a typical retrieval environment answers are either yes or
no i.e. on or off. On the other hand fuzzy logic can bring in a degree of match vs. a crisp
i.e. strict match. This too is looked at and explored in much detail showing how it can be
applied to information retrieval. Fuzzy logic is often times considered a soft computing
application and this book explores how IR with fuzzy logic and its membership functions as
weights can help indexing querying and matching. Since fuzzy set theory and logic is explored
in IR systems the explanation of where the fuzz is ensues. The concept of relevance feedback
including pseudorelevance feedback is explored for the various models of IR. For the extended
Boolean model the use of genetic algorithms for relevance feedback is delved into. The concept
of query expansion is explored using rough set theory. Various term relationships is modeled
and presented and the model extended for fuzzy retrieval. An example using the UMLS terms is
also presented. The model is also extended for term relationships beyond synonyms. Finally
this book looks at clustering both crisp and fuzzy to see how that can improve retrieval
performance. An example is presented to illustrate the concepts.
