Managing uncertainty and vagueness in description logics for the Semantic Web

Lukasiewicz, Thomas; Straccia, Umberto

doi:10.1016/j.websem.2008.04.001

Cited by 328 publications

(114 citation statements)

References 98 publications

(166 reference statements)

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“…Fuzzy extensions of DLs have been studied for more than a decade, and the literature on the topic is very extensive (see [26] for a survey). However, most of those approaches are based on the simple Zadeh semantics with truth values ranging over the interval [0, 1] of rational numbers, where conjunction is interpreted as the minimum.…”

Section: Introductionmentioning

confidence: 99%

The Complexity of Lattice-Based Fuzzy Description Logics

Borgwardt

Peñaloza

2012

J Data Semant

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We study the complexity of reasoning in fuzzy description logics with semantics based on finite residuated lattices. For the logic SHI, we show that deciding satisfiability and subsumption of concepts, with or without a TBox, are ExpTime-complete problems. In ALCHI and a variant of SI, these decision problems become PSpace-complete when restricted to acyclic TBoxes. This matches the known complexity bounds for reasoning in crisp description logics between ALC and SHI.

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Section: Introductionmentioning

confidence: 99%

The Complexity of Lattice-Based Fuzzy Description Logics

Borgwardt

Peñaloza

2012

J Data Semant

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“…For instance, Lukasiewicz and Straccia [25] summarized approaches to manage uncertainty and vagueness in description logics for the semantic web. Ding et al [26] used Bayesian theories to manage context uncertainties; a similar approach was adopted by Yang et al [27].…”

Section: Related Workmentioning

confidence: 99%

Towards a Hybrid Approach to Context Reasoning for Underwater Robots

Martínez-Ortega

Rubio

2017

Applied Sciences

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Abstract:Ontologies have been widely used to facilitate semantic interoperability and serve as a common information model in many applications or domains. The Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project, aiming to facilitate coordination and cooperation between heterogeneous underwater vehicles, also adopts ontologies to formalize information that is necessarily exchanged between vehicles. However, how to derive more useful contexts based on ontologies still remains a challenge. In particular, the extreme nature of the underwater environment introduces uncertainties in context data, thus imposing more difficulties in context reasoning. None of the existing context reasoning methods could individually deal with all intricacies in the underwater robot field. To this end, this paper presents the first proposal applying a hybrid context reasoning mechanism that includes ontological, rule-based, and Multi-Entity Bayesian Network (MEBN) reasoning methods to reason about contexts and their uncertainties in the underwater robot field. The theoretical foundation of applying this reasoning mechanism in underwater robots is given by a case study on the oil spill monitoring. The simulated reasoning results are useful for further decision-making by operators or robots and they show that the consolidation of different reasoning methods is a promising approach for context reasoning in underwater robots.

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“…Furthermore, based on descriptive models, the reasoning over uncertain information is also a tough issue for context-aware event recognition. Currently, a number of approaches for uncertain information management were proposed founded on description logics [11,12,13,14,15]. For instance, Almeida et al [12] proposed an ontology-based approach for modeling and managing the user context with consideration of uncertainty and vagueness in contextual data.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Context-Aware Event Recognition Based on Markov Logic Networks

Liu

Deng

2017

Sensors

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Event recognition in smart spaces is an important and challenging task. Most existing approaches for event recognition purely employ either logical methods that do not handle uncertainty, or probabilistic methods that can hardly manage the representation of structured information. To overcome these limitations, especially in the situation where the uncertainty of sensing data is dynamically changing over the time, we propose a multi-level information fusion model for sensing data and contextual information, and also present a corresponding method to handle uncertainty for event recognition based on Markov logic networks (MLNs) which combine the expressivity of first order logic (FOL) and the uncertainty disposal of probabilistic graphical models (PGMs). Then we put forward an algorithm for updating formula weights in MLNs to deal with data dynamics. Experiments on two datasets from different scenarios are conducted to evaluate the proposed approach. The results show that our approach (i) provides an effective way to recognize events by using the fusion of uncertain data and contextual information based on MLNs and (ii) outperforms the original MLNs-based method in dealing with dynamic data.

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Managing uncertainty and vagueness in description logics for the Semantic Web

Cited by 328 publications

References 98 publications

The Complexity of Lattice-Based Fuzzy Description Logics

The Complexity of Lattice-Based Fuzzy Description Logics

Towards a Hybrid Approach to Context Reasoning for Underwater Robots

Dynamic Context-Aware Event Recognition Based on Markov Logic Networks

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