A Modular Inference System for Probabilistic Description Logics

Cota, Giuseppe; Riguzzi, Fabrizio; Zese, Riccardo; Bellodi, Elena; Lamma, Evelina

doi:10.1007/978-3-030-00461-3_6

Cited by 3 publications

(10 citation statements)

References 17 publications

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“…We are also studying the possibility of made TRILL systems incremental and following a more pay-as-you-go model. The optimizations presented here, and these future directions are aimed at the realization of a Semantic Web framework where several reasoners are made easily interchangeable because all bundled in a single reasoner, BUNDLE [12]. The reasoners are used to collect justifications while BUNDLE oversees the computation of the probability of the query from them.…”

Section: Discussionmentioning

confidence: 99%

“…Probabilistic systems that can perform inference under DISPONTE are BUNDLE [12,13] and the TRILL framework. The first one is implemented in Java and it can exploit several non-probabilistic reasoners, the latter is a framework, written in Prolog, which contains three reasoners, namely, (i) TRILL [14,10], able to collect the set of all justifications and compute the probability of queries, (ii) TRILL [14,10], which implements in Prolog the tableau algorithm defined by Baader and Peñaloza [15,16] for returning the pinpoint-ing formula instead of the set of justifications, and (iii) TOR-NADO [17], which is similar to TRILL but represents the pinpointing formula in a way that can be directly used to compute the probability of the query.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing a tableau reasoner and its implementation in Prolog

Zese

Cota²

2021

Journal of Web Semantics

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing a tableau reasoner and its implementation in Prolog

Zese

Cota²

2021

Journal of Web Semantics

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“…BUNDLE (“BDDs for uncertain reasoning on description logic theories”) 29,30 is a system for performing probabilistic inference from DISPONTE knowledge bases by means of Binary Decision Diagrams (BDDs): BUNDLE computes the probability of queries from a covering set of explanations returned by an underlying reasoner. Initially built for exploiting Pellet as an underlying reasoner to collect explanations, it was extended 31 in order to: (1) interface with Fact++, JFact, and HermiT in the same way as Pellet; (2) interface with TRILL, TRILL

{}^{\mathrm{P}}

and TORNADO which, instead, do not require the conversion of explanations into a BDD.…”

Section: Introductionmentioning

confidence: 99%

A web application for reasoning on probabilistic description logics knowledge bases

Zese

Bellodi

2023

Softw Pract Exp

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The aim of the Semantic Web is making information and resources from the Web automatically processable by machines. Usually, the uncertainty characterizing much of this information is addressed by means of a probabilistic semantics. Following the vision of a “Probabilistic Semantic Web”, a plethora of probabilistic semantics have been proposed: some of them change the syntax and/or the semantics itself of the knowledge representation language, others allow one to annotate axioms of a knowledge base with a probability value. Among the latter, the DISPONTE semantics exploits probabilistic annotations to extend query answering with the capability of returning the probability of a query being true in a domain. In order to promote the adoption of Probabilistic Semantic Web we first developed BUNDLE, a framework that can exploit different underlying (probabilistic and non‐probabilistic) reasoners to perform probabilistic inference under the DISPONTE semantics. In this paper we present a web application for BUNDLE, to show how DISPONTE is easily usable even in already established applications and systems. It allows users to query a DISPONTE knowledge base written or uploaded directly in the application interface by using just a web browser, without the need to install any software on their machine. It is accessible on the web at https://bundle.ml.unife.it/ and also provides some examples for familiarizing with the application. The results of a usability evaluation involving human participants are also reported, showing the relevance and the practical impact of the tool and possible ways for improvement.

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“…The first version of BUNDLE was able to execute justification finding by exploiting the Pellet reasoner only [33]. Then it was extended to exploit different non-probabilistic OWL reasoners and approaches for justification finding [7]. In particular, it embeds Pellet, Hermit, Fact++ and JFact as OWL reasoners, and three justification generators, namely GlassBox (only for Pellet), BlackBox and OWL Explanation.…”

Section: Introductionmentioning

confidence: 99%

“…"-" means that the execution timed out (600 s). To further test the various settings of the BUNDLE framework on real world KBs, we have conducted a test using the Foundational Model of Anatomy Ontology (FMA for short)7 . FMA is a KB for biomedical informatics that models the phenotypic structure of the human body anatomy.…”

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confidence: 99%

A Framework for Reasoning on Probabilistic Description Logics

Cota,

Zese,

Bellodi

et al. 2020

Preprint

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While there exist several reasoners for Description Logics, very few of them can cope with uncertainty. BUNDLE is an inference framework that can exploit several OWL (non-probabilistic) reasoners to perform inference over Probabilistic Description Logics. In this chapter, we report the latest advances implemented in BUN-DLE. In particular, BUNDLE can now interface with the reasoners of the TRILL system, thus providing a uniform method to execute probabilistic queries using different settings. BUNDLE can be easily extended and can be used either as a standalone desktop application or as a library in OWL API-based applications that need to reason over Probabilistic Description Logics. The reasoning performance heavily depends on the reasoner and method used to compute the probability. We provide a comparison of the different reasoning settings on several datasets.

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A Modular Inference System for Probabilistic Description Logics

Cited by 3 publications

References 17 publications

Optimizing a tableau reasoner and its implementation in Prolog

Optimizing a tableau reasoner and its implementation in Prolog

A web application for reasoning on probabilistic description logics knowledge bases

A Framework for Reasoning on Probabilistic Description Logics

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