Logics for approximate and strong entailments

We consider two kinds of similarity-based reasoning and formalise them in a logical setting. In one case, we are led by the principle that conclusions can be drawn even if they are only approximately correct. This leads to a graded approximate entailment, which is weaker than classical entailment. In the other case, we follow the principle that conclusions must remain correct even if the assumptions are slightly changed. This leads to a notion of a graded strong entailment, which is stronger than classical entailment. We develop two logical calculi based on the notions of approximate and of strong entailment, respectively. © 2011 Elsevier B.V.The authors acknowledge partial support of the bilateral Austrian-Spanish project HA2008-0017 and the Eurocores-LogICCC ESF project LoMoReVI. Esteva and Godo also acknowledge partial support of the Spanish project FFI2008-03126-E/FILO and Rodrıguez acknowledges the projects CyT-UBA X484 and the research CONICET program PIP 12-200801-02543 2009-2011. Finally, Esteva, Godo and Rodrıguez also acknowledge partial support of the MaToMUVI project (PIRSES-GA-2009- 247584).Peer Reviewe

Tractable approximate deduction for OWL

Acknowledgements This work has been partially supported by the European project Marrying Ontologies and Software Technologies (EU ICT2008-216691), the European project Knowledge Driven Data Exploitation (EU FP7/IAPP2011-286348), the UK EPSRC project WhatIf (EP/J014354/1). The authors thank Prof. Ian Horrocks and Dr. Giorgos Stoilos for their helpful discussion on role subsumptions. The authors thank Rafael S. Gonçalves et al. for providing their hotspots ontologies. The authors also thank BoC-group for providing their ADOxx Metamodelling ontologies.Peer reviewedPostprin

FALCON: Faithful Neural Semantic Entailment over ALC Ontologies

Many ontologies, i.e., Description Logic (DL) knowledge bases, have been developed to provide rich knowledge about various domains, and a lot of them are based on ALC, i.e., a prototypical and expressive DL, or its extensions. The main task that explores ALC ontologies is to compute semantic entailment. We developed FALCON, a Fuzzy ALC Ontology Neural reasoner, which uses fuzzy logic operators to generate model structures for arbitrary ALC ontologies, and uses multiple model structures to compute faithful semantic entailments. Theoretical results show that FALCON faithfully approximates semantic entailment over ALC ontologies and therefore endows neural networks with world models and the ability to reason over them. Experimental results show that FALCON enables approximate reasoning, paraconsistent reasoning (reasoning with inconsistencies), and improves machine learning in the biomedical domain by incorporating knowledge expressed in ALC

Explanation for defeasible entailment

Explanation facilities are an essential part of tools for knowledge representation and reasoning systems. Knowledge representation and reasoning systems allow users to capture information about the world and reason about it. They are useful in understanding entailments which allow users to derive implicit knowledge that can be made explicit through inferences. Additionally, explanations also assist users in debugging and repairing knowledge bases when conflicts arise. Understanding the conclusions drawn from logic-based systems are complex and requires expert knowledge, especially when defeasible knowledge bases are taken into account for both expert and general users. A defeasible knowledge base represents statements that can be retracted because they refer to information in which there are exceptions to stated rules. That is, any defeasible statement is one that may be withdrawn upon learning of an exception. Explanations for classical logics such as description logics which are well-known formalisms for reasoning about information in a given domain are provided through the notion of justifications. Simply providing or listing the statements that are responsible for an entailment in the classical case is enough to justify an entailment. However, when looking at the defeasible case where entailed statements can be retracted, this is not adequate because the way in which entailment is performed is more complicated than the classical case. In this dissertation, we combine explanations with a particular approach to dealing with defeasible reasoning. We provide an algorithm to compute justification-based explanations for defeasible knowledge bases. It is shown that in order to accurately derive justifications for defeasible knowledge bases, we need to establish the point at which conflicts arise by using an algorithm to come up with a ranking of defeasible statements. This means that only a portion of the knowledge is considered because the statements that cause conflicts are discarded. The final algorithm consists of two parts; the first part establishes the point at which the conflicts occur and the second part uses the information obtained from the first algorithm to compute justifications for defeasible knowledge bases

Fuzzy inequational logic

We present a logic for reasoning about graded inequalities which generalizes the ordinary inequational logic used in universal algebra. The logic deals with atomic predicate formulas of the form of inequalities between terms and formalizes their semantic entailment and provability in graded setting which allows to draw partially true conclusions from partially true assumptions. We follow the Pavelka approach and define general degrees of semantic entailment and provability using complete residuated lattices as structures of truth degrees. We prove the logic is Pavelka-style complete. Furthermore, we present a logic for reasoning about graded if-then rules which is obtained as particular case of the general result

An epistemic interpretation of paraconsistent weak Kleene logic

This paper extends Fitting’s epistemic interpretation of some Kleene logics to also account for Paraconsistent Weak Kleene logic. To achieve this goal, a dualization of Fitting’s “cut-down” operator is discussed, leading to the definition of a “track-down” operator later used to represent the idea that no consistent opinion can arise from a set including an inconsistent opinion. It is shown that, if some reasonable assumptions are made, the truth-functions of Paraconsistent Weak Kleene coincide with certain operations defined in this track-down fashion. Finally, further reflections on conjunction and disjunction in the weak Kleene logics accompany this paper, particularly concerning their relation with containment logics. These considerations motivate a special approach to defining sound and complete Gentzen-style sequent calculi for some of their four-valued generalizations.Fil: Szmuc, Damián Enrique. Universidad de Buenos Aires. Facultad de Filosofía y Letras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Filosóficas. - Sociedad Argentina de Análisis Filosófico. Instituto de Investigaciones Filosóficas; Argentin

Reasoning in inconsistent prioritized knowledge bases: an argumentative approach

A study of query answering in prioritized ontological knowledge bases (KBs) has received attention in recent years. While several semantics of query answering have been proposed and their complexity is rather well-understood, the problem of explaining inconsistency-tolerant query answers has paid less attention. Explaining query answers permits users to understand not only what is entailed or not entailed by an inconsistent DL-LiteR KBs in the presence of priority, but also why. We, therefore, concern with the use of argumentation frameworks to allow users to better understand explanation techniques of querying answers over inconsistent DL-LiteR KBs in the presence of priority. More specifically, we propose a new variant of Dung’s argumentation frameworks, which corresponds to a given inconsistent DL-LiteR KB. We clarify a close relation between preferred subtheories adopted in such prioritized DL-LiteR setting and acceptable semantics of the corresponding argumentation framework. The significant result paves the way for applying algorithms and proof theories to establish preferred subtheories inferences in prioritized DL-LiteR KBs