How to Evaluate Controlled Natural Languages

This paper presents a general framework how controlled natural languages can be evaluated and compared on the basis of user experiments. The subjects are asked to classify given statements (in the language to be tested) as either true or false with respect to a certain situation that is shown in a graphical notation called "ontographs". A first experiment has been conducted that applies this framework to the language Attempto Controlled English (ACE)

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

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

Application of ontologies in identifying requirements patterns in use cases

Use case specifications have successfully been used for requirements description. They allow joining, in the same modeling space, the expectations of the stakeholders as well as the needs of the software engineer and analyst involved in the process. While use cases are not meant to describe a system's implementation, by formalizing their description we are able to extract implementation relevant information from them. More specifically, we are interested in identifying requirements patterns (common requirements with typical implementation solutions) in support for a requirements based software development approach. In the paper we propose the transformation of Use Case descriptions expressed in a Controlled Natural Language into an ontology expressed in the Web Ontology Language (OWL). OWL's query engines can then be used to identify requirements patterns expressed as queries over the ontology. We describe a tool that we have developed to support the approach and provide an example of usage.This is partly funded by project LATiCES (ref. NORTE-07-0124-FEDER-000062), co-financed by the North Portugal Regional Operational Programme (ON.2 - O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF), and by national funds, through the Portuguese foundation for science and technology (FCT)