Predicting the understandability of OWL inferences

In this paper, we describe a method for predicting the understandability level of inferences with OWL. Specifically, we present a model for measuring the understandability of a multiple-step inference based on the measurement of the understandability of individual inference steps. We also present an evaluation study which confirms that our model works relatively well for two-step inferences with OWL. This model has been applied in our research on generating accessible explanations for an entailment of OWL ontologies, to determine the most understandable inference among alternatives, from which the final explanation is generated

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

Human Reasoning and Description Logics: Applying Psychological Theory to Understand and Improve the Usability of Description Logics

Description Logics (DLs) are now the most commonly used ontology languages, in part because of the development of the Web Ontology Language (OWL) standards. Yet it is accepted that DLs are difficult to comprehend and work with, particularly for ontology users who are not computer scientists. The Manchester OWL Syntax (MOS) was developed to make DLs more accessible, by using English keywords in place of logic symbols or formal language. Nevertheless, DLs continue to present difficulties, even when represented in MOS. There has been some investigation of what features cause difficulties, specifically in the context of understanding how an entailment (i.e. an inference) follows from a justification (i.e. a minimal subset of the ontology that is sufficient for the entailment to hold), as is required when debugging an ontology. However, there has been little attempt to relate these difficulties to how people naturally reason and use language. This dissertation draws on theories of reasoning from cognitive psychology, and also insights from the philosophy of language, to understand the difficulties experienced with DLs and to make suggestions to mitigate those difficulties. The language features investigated were those known to be commonly used, both on the basis of analyses reported in the literature and after a survey of ontology users. Two experimental studies investigated participants’ ability to reason with DL statements. These studies demonstrate that insights from psychology and the philosophy of language can be used both to understand the difficulties experienced and to make proposals to mitigate those difficulties. The studies suggest that people reason using both the manipulation of syntax and the representation of semantics with mental models; both approaches can lead to errors. Particular difficulties were associated with: functional object properties; negated conjunction; the interaction of negation and the existential or universal restrictions; and nested restrictions. Proposals to mitigate these difficulties include the adoption of new language keywords; tool enhancement, e.g. to provide syntactically alternative expressions; and the introduction during training both of De Morgan’s Laws for conjunction and disjunction, and their analogues for existential and universal restrictions. A third study then investigated the effectiveness of the proposed new keywords; finding that these keywords could mitigate some of the difficulties experienced. Apart from the immediate applicability of these results to DLs, the approach taken in this dissertation could be extended widely to computer languages, including languages for interacting with databases and with Linked Data. Additionally, based on the experience of the three studies, the dissertation makes some methodological recommendations which are relevant to a range of human-computer interaction studies

Automated Deduction – CADE 28

This open access book constitutes the proceeding of the 28th International Conference on Automated Deduction, CADE 28, held virtually in July 2021. The 29 full papers and 7 system descriptions presented together with 2 invited papers were carefully reviewed and selected from 76 submissions. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience. The papers are organized in the following topics: Logical foundations; theory and principles; implementation and application; ATP and AI; and system descriptions