A Bayesian Extension of the Description Logic $$\mathcal {ALC}$$

Botha, Leonard; Meyer, Thomas; Peñaloza, Rafael

doi:10.1007/978-3-030-19570-0_22

Cited by 6 publications

(5 citation statements)

References 15 publications

(19 reference statements)

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“…However, our tableaux-based algorithm has the potential to behave better in practical scenarios. This work details and deepens results that have previously been presented in (Botha 2018;Botha et al 2018;Botha et al 2019)…”

Section: Introductionsupporting

confidence: 80%

The Probabilistic Description Logic

Botha

Meyer

Peñaloza³

2020

Theory and Practice of Logic Programming

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Description logics (DLs) are well-known knowledge representation formalisms focused on the representation of terminological knowledge. Due to their first-order semantics, these languages (in their classical form) are not suitable for representing and handling uncertainty. A probabilistic extension of a light-weight DL was recently proposed for dealing with certain knowledge occurring in uncertain contexts. In this paper, we continue that line of research by introducing the Bayesian extension of the propositionally closed DL . We present a tableau-based procedure for deciding consistency and adapt it to solve other probabilistic, contextual, and general inferences in this logic. We also show that all these problems remain ExpTime-complete, the same as reasoning in the underlying classical .

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Section: Introductionsupporting

confidence: 80%

The Probabilistic Description Logic

Botha

Meyer

Peñaloza³

2020

Theory and Practice of Logic Programming

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“…We used the IoT infrastructure techniques such as NodeMCU wireless module for configuring the IoT sensors and actuators Light Dependent Resistor (LDR) sensor for detecting the light mode, lm35 temperature sensor, GPS location sensor, and IR Transmitter sensor, all of these are developed across Arduino environment with integration of python programming language. Also, we use an ontology, RDF, Description Logics www.ijacsa.thesai.org (DL) [17], Semantic Web Rule Language (SWRL) for processing the collected sensor data [18].…”

Section: Proposed Work Discussionmentioning

confidence: 99%

Semantic Architecture for Modelling and Reasoning IoT Data Resources based on SPARK

Salama¹,

Masoud²,

Al-Feel³

2020

IJACSA

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Electronic Internet-of-Things is one of the foremost valuable techniques today. Through it, everything everywhere the globe became connected and intelligent, eliminating the wants to human-to-human interaction to perform tasks. This by changing all of those objects like humans, machines, devices and something around to be simply an internet Protocol (IP) to be expressed within the network environment through completely different sensors and actuators devices which might facilitate the interaction between all of them. These different types of sensors generate a large volume of various information and data. This type of sensor information created it generally useless because of the heterogeneity and lack of interoperability of it that represents it in unstructured form. So, investing from semantic internet techniques might handle these main challenges that face the IoT applications. Hence, the main contribution behind this research aims to boost the performance and quality of sensors information retrieved from IoT resources and applications by using semantic web technologies to resolve the matter of heterogeneity and interoperability and then convert the unstructured sensor data to structured form to realize the next level of investing of sensors employed in IoT applications. Also, the aim through this research to improve the performance of the tremendous amount of information that represents the demonstrated IoT information utilizing Big Data techniques such as Spark and its query language that's named SPARK-SQL as a streaming inquiry language for a colossal amount of information. The proposed architecture demonstrated that utilizing the semantic techniques to model the streaming sensors data improve the value of information and permit us to gather unused information. Moreover, the improvement by using SPARK leads to extend the performance of utilizing this sensor information in terms of the time retrieval of running queries, particularly when running the same queries utilizing the conventional SPARQL inquiry language.

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“…Likewise, considering TBoxes exclusively, without the addition of ABoxes was a design choice to simplify the introduction of the formalism. However, our framework can be combined with other (potentially more expressive) logics, akin to what was done for Bayesian DLs (Ceylan and Peñaloza 2017;Botha, Meyer, and Peñaloza 2019). Building those extensions introduces further problems (e.g., consistency) that would need to be studied in detail as well.…”

Section: Discussionmentioning

confidence: 99%

“…To model different aspects of knowledge domains where uncertainty is unavoidable, such as in the bio-medical sciences, many probabilistic extensions of DLs have been proposed in the literature (Lukasiewicz and Straccia 2008;Niepert, Noessner, and Stuckenschmidt 2011;Gutiérrez-Basulto et al 2017;Riguzzi et al 2015). Among them, a prominent example are Bayesian DLs (Ceylan and Peñaloza 2017;2014b;Botha, Meyer, and Peñaloza 2019;d'Amato, Fanizzi, and Lukasiewicz 2008), which provide a means for expressing complex probabilistic and logical dependencies between axioms. For example, in these logics it is easy to express that two axioms must always appear together, or that if one axiom holds, then the likelihood of another one holding is some probability p.…”

Section: Introductionmentioning

confidence: 99%

Reasoning with Contextual Knowledge and Influence Diagrams

Acar

Peñaloza

2020

Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning

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Influence diagrams (IDs) are well-known formalisms, which extend Bayesian networks to model decision situations under uncertainty. Although they are convenient as a decision theoretic tool, their knowledge representation ability is limited in capturing other crucial notions such as logical consistency. In this article, we complement IDs with the light-weight description logic (DL) EL to overcome such limitations. We consider a setup where DL axioms hold in some contexts, yet the actual context is uncertain. The framework benefits from the convenience of using DL as a domain knowledge representation language and the modelling strength of IDs to deal with decisions over contexts in the presence of contextual uncertainty. We define related reasoning problems and study their computational complexity.

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A Bayesian Extension of the Description Logic $$\mathcal {ALC}$$

Cited by 6 publications

References 15 publications

The Probabilistic Description Logic

The Probabilistic Description Logic

Semantic Architecture for Modelling and Reasoning IoT Data Resources based on SPARK

Reasoning with Contextual Knowledge and Influence Diagrams

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