Pronto: A Non-monotonic Probabilistic Description Logic Reasoner

Klinov, Pavel

doi:10.1007/978-3-540-68234-9_66

Cited by 37 publications

(30 citation statements)

References 7 publications

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“…Terminological probabilistic knowledge is expressed using conditional constraints of the form (D|C) [l, u] that informally mean "generally, if an object belongs to C, then it belongs to D with a probability in [l, u]". PRONTO (Klinov, 2008;Klinov and Parsia, 2011) is a system that performs inference under this semantics. Similarly to (Jaeger, 1994), the terminological knowledge is interpreted statistically while the assertional knowledge is interpreted in an epistemic way by assigning degrees of beliefs to assertions.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Description Logics under the distribution semantics

Riguzzi

Bellodi

Lamma

et al. 2015

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Representing uncertain information is crucial for modeling real world domains. In this paper we present a technique for the integration of probabilistic information in Description Logics (DLs) that is based on the distribution semantics for probabilistic logic programs. In the resulting approach, that we called DISPONTE, the axioms of a probabilistic knowledge base (KB) can be annotated with a real number between 0 and 1. A probabilistic knowledge base then defines a probability distribution over regular KBs called worlds and the probability of a given query can be obtained from the joint distribution of the worlds and the query by marginalization. We present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases. The algorithm exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed. The experimentation of BUNDLE on probabilistic knowledge bases shows that it can handle knowledge bases of realistic size.

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Section: Related Workmentioning

confidence: 99%

Probabilistic Description Logics under the distribution semantics

Riguzzi

Bellodi

Lamma

et al. 2015

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“…Since they are all defined over ( ) 3 . P I  Proposition 5: Associative laws for union and intersection is obvious in the context of ontologies 1) ( ) ( )…”

Section: By the Definition Of The Complement (H C 3 )mentioning

confidence: 99%

“…Fuzzy extensions of OWL have been proposed in [13] as a means of handling uncertainty in OWL ontologies. Pronto [3] is a probabilistic DL reasoner prototype. Pronto is able to represent and reason about uncertainty in OWL ontologies by establishing the probabilistic relationships between OWL classes and probabilistic relationships between an OWL class and an individual.…”

Section: Related Workmentioning

confidence: 99%

An Algebra of Ontologies Approximation under Uncertainty

Kana¹,

Akinkunmi²

2014

IJIS

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Ontologies are widely used in modeling the real world for the purpose of information sharing and reasoning. Traditional ontologies contain only concepts and relations that describe asserted facts about the world. Modeling in a dynamic world requires taking into consideration the uncertainty that may arise in the domain. In this paper, the concept of soft sets initiated by Molodtsov and the concept of rough sets introduced by Pawlack are used to define a way of instantiating ontologies of vague domains. We define ontological algebraic operations and their properties while taking into consideration the uncertain nature of domains. We show that, by doing so, intra ontological operations and their properties are preserved and formalized as operations in a vague set of objects and can be proved algebraically.

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“…Several probabilistic description logics have appeared in the literature [13,17]; here we just indicate a few representative proposals.…”

Section: Probabilistic Description Logics and Cralcmentioning

confidence: 99%

Link prediction using a probabilistic description logic

2013

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Due to the growing interest in social networks, link prediction has received significant attention. Link prediction is mostly based on graph-based features, with some recent approaches focusing on domain semantics. We propose algorithms for link prediction that use a probabilistic ontology to enhance the analysis of the domain and the unavoidable uncertainty in the task (the ontology is specified in the probabilistic description logic crALC). The scalability of the approach is investigated, through a combination of semantic assumptions and graph-based features. We evaluate empirically our proposal, and compare it with standard solutions in the literature.

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Pronto: A Non-monotonic Probabilistic Description Logic Reasoner

Cited by 37 publications

References 7 publications

Probabilistic Description Logics under the distribution semantics

Probabilistic Description Logics under the distribution semantics

An Algebra of Ontologies Approximation under Uncertainty

Link prediction using a probabilistic description logic

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