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008 220601s2017 sz | s |||| 0|eng d
020 _a9783031023071
_9978-3-031-02307-1
024 7 _a10.1007/978-3-031-02307-1
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM043000
_2bisacsh
072 7 _aUKN
_2thema
082 0 4 _a004.6
_223
100 1 _aKraft, Donald H.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987937
245 1 0 _aFuzzy Information Retrieval
_h[electronic resource] /
_cby Donald H. Kraft, Erin Colvin.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXVII, 63 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Information Concepts, Retrieval, and Services,
_x1947-9468
505 0 _aPreface -- Acknowledgments -- Introduction to Information Retrieval -- Modeling -- Source of Weights -- Relevance Feedback and Query Expansion -- Clustering for Retrieval -- Uses of Information Retrieval Today -- Bibliography -- Author Biographies.
520 _aInformation 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.
650 0 _aComputer networks .
_931572
650 1 4 _aComputer Communication Networks.
_987938
700 1 _aColvin, Erin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987940
710 2 _aSpringerLink (Online service)
_987941
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031011795
776 0 8 _iPrinted edition:
_z9783031034350
830 0 _aSynthesis Lectures on Information Concepts, Retrieval, and Services,
_x1947-9468
_987944
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02307-1
912 _aZDB-2-SXSC
942 _cEBK
999 _c86175
_d86175