Catherine Dolbear

Rabbit: developing a control natural language for authoring ontologies

The mathematical nature of description logics has meant that domain experts find them hard to understand. This forms a significant impediment to the creation and adoption of ontologies. This paper describes Rabbit, a Controlled Natural Language that can be translated into OWL with the aim of achieving both comprehension by domain experts and computational preciseness. We see Rabbit as complementary to OWL, extending its reach to those who need to author and understand domain ontologies but for whom descriptions logics are difficult to comprehend even when expressed in more user-friendly forms such as the Manchester Syntax. The paper outlines the main grammatical aspects of Rabbit, which can be broadly classified into declarations, concept descriptions and definitions, and elements to support interoperability between ontologies. The paper also describes the human subject testing that has been performed to date and indicates the changes currently being made to the language following this testing. Further modifications have been based on practical experience of the application of Rabbit for the development of operational ontologies in the domain of topography.

Supporting domain experts to construct conceptual ontologies: A holistic approach

Abstract A recent trend in ontology engineering research aims at encouraging the active participation of domain experts in the ontology creation process. Ontology construction methodologies together with appropriate tools and technologies, such as controlled natural languages, semantic wikis, intelligent user interfaces and social computing, are being proposed to enable the direct input from domain experts and to minimize the dependency on knowledge engineers at every step of ontology development. The time is ripe for consolidating methodological and technological advancements to create intuitive ontology engineering tools which can make Semantic Web technologies usable by a wide variety of people without formal knowledge engineering skills. A novel, holistic approach to facilitate the involvements of domain experts in the ontology authoring process is presented here. It integrates (i) an ontology construction methodology, (ii) the use of a controlled natural language, and (iii) appropriate tool support. The integrated approach is illustrated with the design, implementation and evaluation of ROO – a unique ontology authoring tool which combines intelligent techniques to assist domain experts in constructing ontologies. The benefits and limitations of the proposed approach are analyzed based on user studies with ROO. A broader discussion is provided pointing at issues to be taken into account when assisting the involvement of domain experts in ontology construction.

Rabbit to OWL: ontology authoring with a CNL-based tool

Recent work on ontology engineering has seen the adoption of controlled natural languages to ease the process of ontology authoring. However, CNL-based tools still require good knowledge engineering skills to be used efficiently. In this paper presents ROO, an ontology authoring tool that has been designed to cater for the needs of domain experts with little or no ontology engineering experience. ROO combines a CNL-based interface with appropriate tool support based on an ontology construction methodology. We focus on how this tool support is provided in ROO by using and implementing novel aspects of the Rabbit controlled natural language and we refer to an evaluation study that provides empirical evidence in support of using CNL-based techniques to assist ontology authors.

Talking rabbit: a user evaluation of sentence production

Rabbit is a controlled natural language (CNL) designed to aid experts in understanding and authoring domain ontologies. There are three broad types of Rabbit sentence: declarations, axioms and import statements. This paper evaluates the ease with which domain experts without any prior ontology development experience can author declarations and axiom sentences in Rabbit. Participants were asked to author Rabbit sentences about an artificial domain (Planet Zog). The most common error observed was the omission of the every keyword at the beginning of sentences. Another common error was to confuse instance and subclass declarations. Suggested improvements include changes to the Rabbit syntax as well modifications to a CNL authoring environment.

Ontology ontogeny: understanding how an ontology is created and developed

This paper describes the development of a systematic method for creating domain ontologies. We have chosen to explicitly recognise the differing needs of the human domain expert and the machine in our representation of ontologies in two forms: a conceptual and a logical ontology. The conceptual ontology is intended for human understanding and the logical ontology, expressed in description logics, is derived from the conceptual ontology and intended for machine processing. The main contribution of our work is the division of these two stages of ontology development, with emphasis placed on domain experts themselves creating the conceptual ontology, rather than relying on a software engineer to elicit knowledge about the domain. In particular, this paper concentrates on the creation of conceptual ontologies and analyses the success of our methodology when tested by domain experts.