Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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Testing implications of data dependencies
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The Implication Problem for Data Dependencies
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Optimal implementation of conjunctive queries in relational data bases
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The description logic handbook: theory, implementation, and applications
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Data exchange: semantics and query answering
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Machine Learning
OntoBayes: An Ontology-Driven Uncertainty Model
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Expressive probabilistic description logics
Artificial Intelligence
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Managing uncertainty and vagueness in description logics for the Semantic Web
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Hybrid reasoning with rules and ontologies
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P-CLASSIC: a tractable probablistic description logic
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Datalog+/-: A Family of Logical Knowledge Representation and Query Languages for New Applications
LICS '10 Proceedings of the 2010 25th Annual IEEE Symposium on Logic in Computer Science
Providing support for full relational algebra in probabilistic databases
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
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Consistent answers in probabilistic datalog+/--- ontologies
RR'12 Proceedings of the 6th international conference on Web Reasoning and Rule Systems
Ontology-based access to probabilistic data with OWL QL
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
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Datalog+/- is a recently developed family of ontology languages that is especially useful for representing and reasoning over lightweight ontologies, and is set to play a central role in the context of query answering and information extraction for the Semantic Web. It has recently become apparent that it is necessary to develop a principled way to handle uncertainty in this domain; in addition to uncertainty as an inherent aspect of the Web, one must also deal with forms of uncertainty due to inconsistency and incompleteness, uncertainty resulting from automatically processing Web data, as well as uncertainty stemming from the integration of multiple heterogeneous data sources. In this paper, we present two algorithms for answering conjunctive queries over a probabilistic extension of guarded Datalog+/- that uses Markov logic networks as the underlying probabilistic semantics. Conjunctive queries ask: "what is the probability that a given set of atoms hold?". These queries are especially relevant to Web information extraction, since extractors often work with uncertain rules and facts, and decisions must be made based on the likelihood that certain facts are inferred. The first algorithm for answering conjunctive queries is a basic one using classical forward chaining (known as the chase procedure), while the second one is a backward chaining algorithm and works on a specific subset of guarded Datalog+/-; it can be executed as an anytime algorithm for greater scalability.