{"product_id":"9781461375708","title":"The Information Retrieval Series: Advanced Models for the Representation and Retrieval of Information","description":"\u003ch1\u003eThe Information Retrieval Series: Advanced Models for the Representation and Retrieval of Information\u003c\/h1\u003e \u003ch2\u003evan Rijsbergen, Cornelis Joost; Crestani, Fabio; Lalmas, Mounia\u003c\/h2\u003e \u003cp\u003eIn 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. \u003cbr\u003e  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. \u003cbr\u003e  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. \u003cbr\u003e  \u003cem\u003eInformation Retrieval: Uncertainty and Logics\u003c\/em\u003e 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.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2012-12-22\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9781461375708\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-1-4615-5617-6\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 323\u003c\/p\u003e ","brand":"Springer US","offers":[{"title":"Default Title","offer_id":44358922797196,"sku":"9781461375708","price":323.1,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781461375708.jpg?v=1771508293","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9781461375708","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}