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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.

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.

정보제공 :

CONTENTS
List of Figures = ⅸ
List of Tables = xi
Preface = xiii
Contributing Authors = xix
Part Ⅰ Genesis
  1 A non-classical logic for information retrieval / Cornelis Joost van rijsbergen = 3
    1.1 Introduction = 3
    1.2 Classical information retrieval = 4
    1.3 A conditional logic for information retrieval = 8
    1.4 How do we evaluate P(s →q)? = 9
    1.5 Logic of uncertainty = 11
    1.6 Conclusion = 12
    References = 12
Part Ⅱ Logical Models
  2 Toward a broader logical model for information retrieval / Jian-Yun Nie ; Francois Lepage = 17
    2.1 Introduction = 17
    2.2 The necessity to consider situational factors = 19
    2.3 Toward a model of relevance = 25
    2.4 An outline for coping with changes in retrieval situations = 31
    2.5 Concluding remarks and further research = 36
    References = 37
  3 Experiences in informatin retrieval modelling using structured formalisms and modal logic / jean-Pierre Chevallet ; Yves Chiaramella = 39
    3.1 Introduction = 39
    3.2 Basic hypotheses = 40
    3.3 A modal retrieval model = 46
    3.4 A theoretical modal model for information retrieval = 49
    3.5 Operational models = 53
    3.6 Theoretical logic model and operational graph model = 66
    3.7 Conclusion = 68
    References = 70
  4 Preferential models of query by navigation / Peter Bruza ; Bernd van Linder = 73
    4.1 Introduction = 73
    4.2 Information retrieval fundamentals = 77
    4.3 Preferential structures, defaults and preclusions = 79
    4.4 Sound inference rules of preferential structures = 90
    4.5 Related work = 93
    4.6 Conclusions and further research = 94
    References = 95
  5 A flexible framework for multimedia information retrieval / Adrian M u ·· ller = 97
    5.1 Introduction = 97
    5.2 Abductive information retrieval : a framework = 100
    5.3 Comparing deductive and abductive information retrieval = 105
    5.4 The abduction procedure for information retrieval : a definition = 108
    5.5 An application : image retrieval by means of abductive inference = 111
    5.6 Conclusions = 122
    References = 125
  6 The flow of information in information retrieval : towards a general frame-work for the modelling of information retrieval / Mounia Lalmas = 129
    6.1 Introduction = 129
    6.2 Situation theory and its connection to information retrieval modelling = 131
    6.3 Channel theory and its connection to information retrieval modelling = 138
    6.4 Other frameworks for modelling the flow of information in IR = 139
    6.5 A general framework for the modelling of information retrieval = 141
    6.6 Application of the model = 145
    6.7 Conclusion = 148
    References = 148
  7 Mirlog : a logic for multimedia information retrieval / Carlo Meghini ; Fabrizio Sebastiani ; Umberto Straccia = 151
    7.1 Introduction = 151
    7.2 Syntax and classical semantics = 154
    7.3 A relevance semantics = 157
    7.4 Closures = 165
    7.5 Modelling uncertainty = 173
    7.6 Reasoning in MIRLOG = 179
    7.7 Conclusions = 180
    References = 182
Part Ⅲ Uncertainty Models
  8 Semantic information retrieval / Gianni Amati ; Keith van Rijsbergen = 189
    8.1 Introduction to semantic information theory = 189
    8.2 An overview from the information retrieval perspective = 190
    8.3 The notion of information content = 194
    8.4 Entropy and information content = 196
    8.5 Duality theory = 211
    8.6 Conclusions = 216
    References = 217
  9 Information retrieval with probabilistic Datalog / Thomas R o ·· lleke ; Norbert Fuhr = 221
    9.1 Introduction = 221
    9.2 Sample document retrieval = 223
    9.3 hypertext structure = 223
    9.4 Logical structure = 224
    9.5 Class hierarchy = 225
    9.6 Terminological knowledge = 225
    9.7 Object-oriented knowledge representation = 227
    9.8 Retrieval and uncertain inference = 230
    9.9 Systax of Datalogp = 234
    9.10 Semantics = 236
    9.11 Evaluation of probabilistic Datalog programs = 240
    9.12 Independence and disjointness assumptions = 242
    9.13 Conclusion and outlook = 243
    References = 244
    9.4 Object-oriented representation of documents. = 229
    9.5 Syntax of probabilistic Datalog. = 236
    10.1 The classical semantics for the term space. = 257
    10.2 Application of the PWS to the term space. = 257
    12.1 The experimental paradigm. = 298