Generating Natural Language Explanations For Entailments In Ontologies

Building an error-free and high-quality ontology in OWL (Web Ontology Language)---the latest standard ontology language endorsed by the World Wide Web Consortium---is not an easy task for domain experts, who usually have limited knowledge of OWL and logic. One sign of an erroneous ontology is the occurrence of undesired inferences (or entailments), often caused by interactions among (apparently innocuous) axioms within the ontology. This suggests the need for a tool that allows developers to inspect why such an entailment follows from the ontology in order to debug and repair it. This thesis aims to address the above problem by advancing knowledge and techniques in generating explanations for entailments in OWL ontologies. We build on earlier work on identifying minimal subsets of the ontology from which an entailment can be drawn---known technically as justifications. Our main focus is on planning (at a logical level) an explanation that links a justification (premises) to its entailment (conclusion); we also consider how best to express the explanation in English. Among other innovations, we propose a method for assessing the understandability of explanations, so that the easiest can be selected from a set of alternatives. Our findings make a theoretical contribution to Natural Language Generation and Knowledge Representation. They could also play a practical role in improving the explanation facilities in ontology development tools, considering especially the requirements of users who are not expert in OWL

The use of data-mining for the automatic formation of tactics

This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques

Datalog± Ontology Consolidation

Knowledge bases in the form of ontologies are receiving increasing attention as they allow to clearly represent both the available knowledge, which includes the knowledge in itself and the constraints imposed to it by the domain or the users. In particular, Datalog ± ontologies are attractive because of their property of decidability and the possibility of dealing with the massive amounts of data in real world environments; however, as it is the case with many other ontological languages, their application in collaborative environments often lead to inconsistency related issues. In this paper we introduce the notion of incoherence regarding Datalog± ontologies, in terms of satisfiability of sets of constraints, and show how under specific conditions incoherence leads to inconsistent Datalog ± ontologies. The main contribution of this work is a novel approach to restore both consistency and coherence in Datalog± ontologies. The proposed approach is based on kernel contraction and restoration is performed by the application of incision functions that select formulas to delete. Nevertheless, instead of working over minimal incoherent/inconsistent sets encountered in the ontologies, our operators produce incisions over non-minimal structures called clusters. We present a construction for consolidation operators, along with the properties expected to be satisfied by them. Finally, we establish the relation between the construction and the properties by means of a representation theorem. Although this proposal is presented for Datalog± ontologies consolidation, these operators can be applied to other types of ontological languages, such as Description Logics, making them apt to be used in collaborative environments like the Semantic Web.Fil: Deagustini, Cristhian Ariel David. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Falappa, Marcelo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Simari, Guillermo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentin

Prioritized base Debugging in Description Logics

International audienceThe problem investigated is the identification within an input knowledge base of axioms which should be preferably discarded (or amended) in order to restore consistency, coherence, or get rid of undesired consequences. Most existing strategies for this task in Description Logics rely on conflicts, either computing all minimal conflicts beforehand, or generating conflicts on demand, using diagnosis. The article studies how prioritized base revision can be effectively applied in the former case. The first main contribution is the observation that for each axiom appearing in a minimal conflict, two bases can be obtained for a negligible cost, representing what part of the input knowledge must be preserved if this axiom is discarded or retained respectively, and which may serve as a basis to obtain a semantically motivated preference relation over these axioms. The second main contributions is an algorithm which, assuming this preference relation is known, selects some of the maximal consistent/coherent subsets of the input knowledge base accordingly, without the need to compute all of of them

Persuasive Explanation of Reasoning Inferences on Dietary Data

Explainable AI aims at building intelligent systems that are able to provide a clear, and human understandable, justification of their decisions. This holds for both rule-based and data-driven methods. In management of chronic diseases, the users of such systems are patients that follow strict dietary rules to manage such diseases. After receiving the input of the intake food, the system performs reasoning to understand whether the users follow an unhealthy behaviour. Successively, the system has to communicate the results in a clear and effective way, that is, the output message has to persuade users to follow the right dietary rules. In this paper, we address the main challenges to build such systems: i) the natural language generation of messages that explain the reasoner inconsistency; ii) the effectiveness of such messages at persuading the users. Results prove that the persuasive explanations are able to reduce the unhealthy users’ behaviours