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A Probabilistic Relational Algebra for the Integration of Information Retrieval and Database Systems
 ACM Transactions on Information Systems
, 1994
"... We present a probabilistic relational algebra (PRA) which is a generalization of standard relational algebra. Here tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. Based on intensional semantics, the tuple weights of the result of a PRA expression ..."
Abstract

Cited by 173 (30 self)
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We present a probabilistic relational algebra (PRA) which is a generalization of standard relational algebra. Here tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. Based on intensional semantics, the tuple weights of the result of a PRA expression always confirm to the underlying probabilistic model. We also show for which expressions extensional semantics yields the same results. Furthermore, we discuss complexity issues and indicate possibilities for optimization. With regard to databases, the approach allows for representing imprecise attribute values, whereas for information retrieval, probabilistic document indexing and probabilistic search term weighting can be modelled. As an important extension, we introduce the concept of vague predicates which yields a probabilistic weight instead of a Boolean value, thus allowing for queries with vague selection conditions. So PRA implements uncertainty and vagueness in combination with the...
Probabilistic Datalog: Implementing Logical Information Retrieval for Advanced Applications
 Journal of the American Society for Information Science
, 1999
"... In the logical approach to information retrieval (IR), retrieval is considered as uncertain inference. ..."
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Cited by 49 (8 self)
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In the logical approach to information retrieval (IR), retrieval is considered as uncertain inference.
Execution Performance Issues in FullText Information Retrieval
, 1995
"... The task of an information retrieval system is to identify documents that will satisfy a user's information need. Effective fulfillment of this task has long been an active area of research, leading to sophisticated retrieval models for representing information content in documents and queries and m ..."
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Cited by 19 (0 self)
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The task of an information retrieval system is to identify documents that will satisfy a user's information need. Effective fulfillment of this task has long been an active area of research, leading to sophisticated retrieval models for representing information content in documents and queries and measuring similarity between the two. The maturity and proven effectiveness of these systems has resulted in demand for increased capacity, performance, scalability, and functionality, especially as information retrieval is integrated into more traditional database management environments. In this dissertation we explore a number of functionality and performance issues in information retrieval. First, we consider creation and modification of the document collection, concentrating on management of the inverted file index. An inverted file architecture based on a persistent object store is described and experimental results are presented for inverted file creation and modification. Our architecture provides performance that scales well with document collection size and the database features supported by the persistent object store provide many solutions to issues that arise during integration of information retrieval into more general database environments. We then turn to query evaluation speed and introduce a new optimization technique for statistical ranking retrieval systems that support structured queries. Experimental results from a variety of query sets show that execution time can be reduced by more than 50% wit...
DOLORES: A System for LogicBased Retrieval of Multimedia Objects
 In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
, 1998
"... We describe the design and implementation of a system for logicbased multimedia retrieval. As highlevel logic for retrieval of hypermedia documents, we have developed a probabilistic objectoriented logic (POOL) which supports aggregated objects, different kinds of propositions (terms, classificati ..."
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Cited by 15 (8 self)
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We describe the design and implementation of a system for logicbased multimedia retrieval. As highlevel logic for retrieval of hypermedia documents, we have developed a probabilistic objectoriented logic (POOL) which supports aggregated objects, different kinds of propositions (terms, classifications and attributes) and even rules as being contained in objects. Based on a probabilistic fourvalued logic, POOL uses an implicit open world assumption, allows for closed world assumptions and is able to deal with inconsistent knowledge. POOL programs and queries are translated into probabilistic Datalog programs which can be interpreted by the HySpirit inference engine. For storing the multimedia data, we have developed a new basic IR engine which yields physical data abstraction. The overall architecture and the flexibility of each layer supports logicbased methods for multimedia information retrieval.
A Probabilistic NF2 Relational Algebra for Integrated Information Retrieval and Database Systems
 In Proceedings of the 2nd World Conference on Integrated Design and Process Technology
, 1996
"... The integration of information retrieval (IR) and database systems requires a data model which allows for modelling documents as entities, representing uncertainty and vagueness and performing uncertain inference. For this purpose, we present a probabilistic data model based on relations in nonfirst ..."
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Cited by 10 (1 self)
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The integration of information retrieval (IR) and database systems requires a data model which allows for modelling documents as entities, representing uncertainty and vagueness and performing uncertain inference. For this purpose, we present a probabilistic data model based on relations in nonfirst normalform (NF2). Here, tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. Thus, the set of weighted index terms of a document are represented as a probabilistic subrelation. In a similar way, imprecise attribute values are modelled as a setvalued attribute. We redefine the relational operators for this type of relations such that the result of each operator is again a probabilistic NF2 relation, where the weight of a tuple gives the probability that this tuple belongs to the result. By ordering the tuples according to decreasing probabilities, the model yields a ranking of answers like in most IR models. This effect also can be used for ...
A Probabilistic NF2 Relational Algebra for Imprecision in
, 1996
"... We present a probabilistic data model which is based on relations in nonfirstnormalform (NF2). Here, tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. This way, imprecise attribute values are modelled as a probabilistic subrelation. ..."
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Cited by 10 (2 self)
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We present a probabilistic data model which is based on relations in nonfirstnormalform (NF2). Here, tuples are assigned probabilistic weights giving the probability that a tuple belongs to a relation. This way, imprecise attribute values are modelled as a probabilistic subrelation.