The Bayesian Description Logic ${\mathcal{BEL}}$

Botha, Leonard; Meyer, Thomas

doi:10.1007/978-3-319-08587-6_37

Cited by 19 publications

(18 citation statements)

References 21 publications

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“…The description logics [Nardi et al, 2003] is a knowledge representation formalism that can be used as the basis for a semantics. This is implemented in a probabilistic setting in, for example, probabilistic ontologies [Riguzzi et al, 2012[Riguzzi et al, , 2015, probabilistic description logics [Heinsohn, 1994], probabilistic description logic programs [Lukasiewicz, 2005], or the bayesian description logics [Ceylan and Peñaloza, 2014].…”

Section: Discussion Future and Related Workmentioning

confidence: 99%

On Constrained Open-World Probabilistic Databases

Friedman

Broeck

2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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Increasing amounts of available data have led to a heightened need for representing large-scale probabilistic knowledge bases. One approach is to use a probabilistic database, a model with strong assumptions that allow for efficiently answering many interesting queries. Recent work on openworld probabilistic databases strengthens the semantics of these probabilistic databases by discarding the assumption that any information not present in the data must be false. While intuitive, these semantics are not sufficiently precise to give reasonable answers to queries. We propose overcoming these issues by using constraints to restrict this open world. We provide an algorithm for one class of queries, and establish a basic hardness result for another. Finally, we propose an efficient and tight approximation for a large class of queries.

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Section: Discussion Future and Related Workmentioning

confidence: 99%

On Constrained Open-World Probabilistic Databases

Friedman

Broeck

2019

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

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“…Many probabilistic DLs have also been considered in the last decades [16,18,19]. Our approach is closest to Bayesian DLs [5,6] and disponte [25]. The greatest difference with the former lies in the fact that ALCP KBs do not require a complete specification of the probability distribution, but only a set of probabilistic constraints.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, several probabilistic DLs have been developed [18,19]. To handle probabilistic knowledge, many approaches require a complete definition of joint probability distributions (JPD) [5,6,8,16,25]. One approach to avoid a full JPD specification was proposed by Paris [22]: the user gives a partial specification through a set of probabilistic constraints and the partial knowledge is completed by means of the principle of maximum entropy.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Reasoning in the Description Logic $$\mathcal {ALCP}$$ with the Principle of Maximum Entropy

Peñaloza

Potyka

2016

Lecture Notes in Computer Science

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A central question for knowledge representation is how to encode and handle uncertain knowledge adequately. We introduce the probabilistic description logic ALCP that is designed for representing context-dependent knowledge, where the actual context taking place is uncertain. ALCP allows the expression of logical dependencies on the domain and probabilistic dependencies on the possible contexts. In order to draw probabilistic conclusions, we employ the principle of maximum entropy. We provide reasoning algorithms for this logic, and show that it satisfies several desirable properties of probabilistic logics.

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“…An important aspect in modeling context is related to the choice of which kind of information is considered to be fixed and which context dependent. Specifically, for DLs, one can define the assertions in the TBox [1,11], the concepts [3], or both [19,14] as context-dependent. Each choice addresses different needs, and results in differences in the complexity of reasoning.…”

Section: Introductionmentioning

confidence: 99%

Verification of Context-Sensitive Knowledge and Action Bases

Calvanese

Ceylan

Montali

et al. 2014

Logics in Artificial Intelligence

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Abstract. Knowledge and Action Bases (KABs) have been recently proposed as a formal framework to capture the dynamics of systems which manipulate Description Logic (DL) Knowledge Bases (KBs) through action execution. In this work, we enrich the KAB setting with contextual information, making use of different context dimensions. On the one hand, context is determined by the environment using context-changing actions that make use of the current state of the KB and the current context. On the other hand, it affects the set of TBox assertions that are relevant at each time point, and that have to be considered when processing queries posed over the KAB. Here we extend to our enriched setting the results on verification of rich temporal properties expressed in μ-calculus, which had been established for standard KABs. Specifically, we show that under a run-boundedness condition, verification stays decidable and does not incur in any additional cost in terms of worst-case complexity. We also show how to adapt syntactic conditions ensuring run-boundedness so as to account for contextual information, taking into account context-dependent activation of TBox assertions.

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The Bayesian Description Logic ${\mathcal{BEL}}$

Cited by 19 publications

References 21 publications

On Constrained Open-World Probabilistic Databases

On Constrained Open-World Probabilistic Databases

Probabilistic Reasoning in the Description Logic $$\mathcal {ALCP}$$ with the Principle of Maximum Entropy

Verification of Context-Sensitive Knowledge and Action Bases

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