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Probabilistic datalog: Implementing logical information retrieval for advanced applications
 Journal of the American Society for Information Science
, 2000
"... Abstract In the logical approach to information retrieval (IR), retrieval is considered as uncertain inference. Whereas classical IR models are based on propositional logic, we combine Datalog (functionfree Horn clause predicate logic) with probability theory. Therefore, probabilistic weights may ..."
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Cited by 62 (9 self)
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Abstract In the logical approach to information retrieval (IR), retrieval is considered as uncertain inference. Whereas classical IR models are based on propositional logic, we combine Datalog (functionfree Horn clause predicate logic) with probability theory. Therefore, probabilistic weights may be attached to both facts and rules. The underlying semantics extends the wellfounded semantics of modularly stratified Datalog to a possible worlds semantics. By using default independence assumptions with explicit specification of disjoint events, the inference process always yields point probabilities. We describe an evaluation method and present an implementation. This approach allows for easy formulation of specific retrieval models for arbitrary applications, and classical probabilistic IR models can be implemented by specifying the appropriate rules. In comparison to other approaches, the possibility of recursive rules allows for more powerful inferences, and predicate logic gives the expressiveness required for multimedia retrieval. Furthermore, probabilistic Datalog can be used as a query language for integrated information retrieval and database systems.
HySpirit  a Probabilistic Inference Engine for Hypermedia Retrieval in Large Databases
 Proceedings of the 6th International Conference on Extending Database Technology (EDBT
, 1998
"... . HySpirit is a retrieval engine for hypermedia retrieval integrating concepts from information retrieval (IR) and deductive databases. The logical view on IR models retrieval as uncertain inference, for which we use probabilistic reasoning. Since the expressiveness of classical IR models is not suf ..."
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Cited by 43 (10 self)
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. HySpirit is a retrieval engine for hypermedia retrieval integrating concepts from information retrieval (IR) and deductive databases. The logical view on IR models retrieval as uncertain inference, for which we use probabilistic reasoning. Since the expressiveness of classical IR models is not sufficient for hypermedia retrieval, HySpirit is based on a probabilistic version of Datalog. In hypermedia retrieval, different nodes may contain contradictory information; thus, we introduce probabilistic fourvalued Datalog. In order to support fact queries as well as contentbased retrieval, HySpirit is based on an open world assumption, but allows for predicatespecific closed world assumptions. For performing efficient retrieval on large databases, our system provides access to external data. We demonstrate the application of HySpirit by giving examples for retrieval on images, structured documents and large databases. 1 Introduction Due to the advances in hardware, processing of multimed...
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 17 (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.
Information Retrieval Methods For Multimedia Objects
, 2000
"... We describe five major concepts that are essential for multimedia retrieval: uncertain inference addresses vagueness of queries and imprecision of content representations. Predicate logic allows for dealing with spatial and temporal relationships. The document structure has to be considered in order ..."
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Cited by 9 (0 self)
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We describe five major concepts that are essential for multimedia retrieval: uncertain inference addresses vagueness of queries and imprecision of content representations. Predicate logic allows for dealing with spatial and temporal relationships. The document structure has to be considered in order to retrieve the most relevant part of a document in response to a query. Whereas fact retrieval employs an open world assumption, contentbased retrieval should be based on an open world assumption. In order to perform inferences based on the content of multimedia objects, inconsistencies have to be dealt with. Based on these concepts, we present DOLORES, a logicbased multimedia retrieval system with a multilayered architecture. Below the toplevel presentation layer, the semantic layer uses a conceptual model for structured documents which is transformed into a probabilistic objectoriented logic (POOL) supporting aggregated objects, di#erent kinds of propositions (terms, classifications and attributes) and even rules as being contained in objects. This fourvalued logic is translated into probabilistic Datalog which is interpreted by the HySpirit inference engine. Multimedia objects are stored either in a relational database management system or an information retrieval engine.
Topk Query Processing in Probabilistic Databases with NonMaterialized Views
, 2012
"... In this paper, we investigate a novel approach of computing confidence bounds for topk ranking queries in probabilistic databases with nonmaterialized views. Unlike prior approaches, we present an exact pruning algorithm for finding the topranked query answers according to their marginal probabil ..."
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Cited by 8 (4 self)
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In this paper, we investigate a novel approach of computing confidence bounds for topk ranking queries in probabilistic databases with nonmaterialized views. Unlike prior approaches, we present an exact pruning algorithm for finding the topranked query answers according to their marginal probabilities without the need to first materialize all answer candidates via the views. Specifically, we consider conjunctive queries over multiple levels of selectprojectjoin views, the latter of which are cast into Datalog rules, where also the rules themselves may be uncertain, i.e., be valid with some degree of confidence. To our knowledge, this work is the first to address integrated data and confidence computations in the context of probabilistic databases by considering confidence bounds over partially evaluated query answers with firstorder lineage formulas. We further extend our query processing techniques by a toolsuite of scheduling strategies based on selectivity estimation and the expected impact of subgoals on the final confidence of answer candidates. Experiments with large datasets demonstrate drastic runtime improvements over both sampling and decompositionbased methods—even
Query algebra operations for interval probabilities
 In Proceedings of the Iternational Conference on Database and Expert Systems Applications (DEXA). Prague, Czech Republic
"... Abstract. The groundswell for the `00s is imprecise probabilities. Whether the numbers represent the probable location of a GPS device at its next sounding, the inherent uncertainty of an individual expert's probability prediction, or the range of values derived from the fusion of sensor data, ..."
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Cited by 2 (0 self)
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Abstract. The groundswell for the `00s is imprecise probabilities. Whether the numbers represent the probable location of a GPS device at its next sounding, the inherent uncertainty of an individual expert's probability prediction, or the range of values derived from the fusion of sensor data, probability intervals became an important way of representing uncertainty. However, until recently, there has been no robust support for storage and management of imprecise probabilities. In this paper, we define the semantics of traditional query algebra operations of selection, projection, Cartesian product and join, as well as an operation of conditionalization, specific to probabilistic databases. We provide efficient methods for computing the results of these operations and show how they conform to probability theory.