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Information Retrieval: Uncertainty and Logics: Advanced Models for the Representation and Retrieval of Information (The Information Retrieval Series, 4, Band 4) - Hardcover

Buch 4 von 42: The Information Retrieval
 
9780792383024: Information Retrieval: Uncertainty and Logics: Advanced Models for the Representation and Retrieval of Information (The Information Retrieval Series, 4, Band 4)
Hardcover
ISBN 10: 0792383028 ISBN 13: 9780792383024
Verlag: Springer, 1998

Inhaltsangabe

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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Über die Autorin bzw. den Autor

Professor Mounia Lalmas has taken up in September 2008 the position of Research Chair in Information Retrieval at the University of Glasgow funded by Microsoft Research/Royal Academy of Engineering. She was previously a Professor of Information Retrieval at Queen Mary, University of London, which she joined in 1999 as a lecturer. Prior to this, she was a Research Scientist at the University of Dortmund in 1998, a Lecturer from 1995 to 1997 and a Research Fellow from 1997 to 1998 at the University of Glasgow, where she received her PhD in 1996. Her research focuses on the development and evaluation of intelligent access to interactive heterogeneous and complex information repositories, and covering a wide range of domains such as Web, XML, and MPEG-7. From 2002 until 2007, she co-led with Norbert Fuhr the Evaluation Initiative for XML Retrieval (INEX), a large-scale project with over 80 participating organizations worldwide, which was responsible for defining the nature of XML retrieval, and how it should be evaluated. She has given numerous presentations and lectures on XML retrieval and evaluation, for instance at CIKM, SIGIR and ESSIR. She is now working on technologies for aggregated search and bridging the digital divide. She is also currently getting back into theoretical information retrieval where she is looking at the use of quantum theory to model interactive information retrieval. She is currently the ACM SIGIR Vice-Chair. She was the workshop co-chair at SIGIR 2004 and 2006, mentoring chair at SIGIR 2009, PR (co-) chair at CIKM 2008 and WI/IAT 2009, PC chair at ECIR 2006, vice co-chair for the XML and Web Data track at WWW 2009, and general co-chair of IIiX 2008. She serves on the TOIS, IP&M and IR journal editorial boards.

Von der hinteren Coverseite

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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Verlag
Springer
Erscheinungsdatum
1998
Sprache
Englisch
ISBN 10 
0792383028
ISBN 13 
9780792383024
Einband
Gebundene Ausgabe
Anzahl der Seiten
344
Herausgeber
Van Rijsbergen Cornelis Joost, Crestani Fabio, Lalmas Mounia
Kontakt zum Hersteller
Nicht verfügbar
Verantwortliche Person
Nicht verfügbar