2008 InvolvingDomainExpertsinAuthori

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Subject Headings: Ontology Engineering; Domain Expert; Knowledge Engineer

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Author Keywords

Ontology Authoring, Controlled Natural Language Interfaces, Evaluation of Ontology Building Tools, Geographical Ontologies

Abstract

The process of authoring ontologies requires the active involvement of domain experts who should lead the process, as well as providing the relevant conceptual knowledge. However, most domain experts lack knowledge modelling skills and find it hard to follow logical notations in OWL. This paper presents ROO, a tool that facilitates domain experts’ definition of ontologies in OWL by allowing them to author the ontology in a controlled natural language called Rabbit. ROO guides users through the ontology construction process by following a methodology geared towards domain experts’ involvement in ontology authoring, and exploiting intelligent user interfaces techniques. An evaluation study has been conducted comparing ROO against another popular ontology authoring tool. Participants were asked to create ontologies based on hydrology and environment modelling scenarios related to real tasks at the mapping agency of Great Britain. The study is discussed, focusing on the usability and usefulness of the tool, and the quality of the resultant ontologies.

The need to construct ontologies – ranging from small domain ontologies to large ontologies linked to legacy datasets – hinders the ability and willingness of organisations to apply Semantic Web (SW) technologies to large-scale data integration and sharing initiatives [1,7,9]. This is due to the time and effort required to create ontologies [1,19]. Most ontology construction tools aggravate the situation because they are designed to be used by specialists with appropriate knowledge engineering and logic skills, but who may lack the necessary domain expertise to create the relevant ontologies. At present, it is knowledge engineers who usually drive the ontology authoring process, which creates an extra layer of bureaucracy in the development cycle [19]. Furthermore, this knowledge engineer led approach can hinder the ontology construction process because the domain expert and domain knowledge may become secondary to the process of efficient knowledge modelling. This is especially true where the domain expert has no understanding of the languages and tools used to construct the ontology. The development of approaches that facilitate the engagement of domain experts in the ontology construction process can lead to a step change in the deployment of the Semantic Web in the public and industrial sector.

Such an approach, drawn upon extensive experience in creating topographic ontologies at Ordnance Survey, the mapping agency of Great Britain, is described here. Ordnance Survey is developing a topographic domain ontology to empower the integration and reuse of their heterogeneous topographic data sets with third party data [9]. At the heart of Ordnance Survey’s ontology development process is the active involvement of domain experts [20]. They construct conceptual ontologies that record domain knowledge in a human readable form with appropriate formality using a controlled language, Rabbit 2 [14], that is translated into OWL DL [8].

The paper presents ROO (R abbit to O WL O ntology authoring), a user-friendly tool that guides the authoring of a conceptual ontology which is then converted to a logical ontology in OWL. The distinctive characteristics of our approach ar e: (a) catering for the needs of domain experts without knowledge engineering skills; (b) exploiting techniques from inte lligent user interfaces to assist the ontology construction process by following an ontology authoring methodology (the current implementation follows the methodology used at Ordnance Survey for developing several large ontologies with the active involvement of domain experts [20]); (c) providing an intuitive interface to enter knowledge constructs in Rabbit. We describe an experimental study that examines the degree to which domain experts (i.e. not knowledge en gineers) can build ontologies 3 following real scenarios base d on work at Ordnance Survey.

An analysis of related work (§2) positio ns ROO in the relevant SW research. §3 presents the ROO tool and gives illustrative ex amples of user interaction taken from an experimental study reported in §4. §5 discusses the findings of the study, and outline implications for SW research.

2. Related Work

Recent developments of ontology authoring tools are increasingly recognising the need to cater for users without knowledge engineering skills. Controlled language (CL) interfaces have been provided for entering knowledge constructs in an intuitive way close to Natural Language (NL) interface (see [11,23] for recent reviews). ROO builds on the strengths and minimises the usability limitations of existing CL tools. Positive usability aspects have been followed in the design of ROO , such as: look ahead to provide suggestions by guessing what constructs the users might enter [24]; showing the parsed structure to help the user recognise correct sentence patterns ([10,21,26]); providing a flexible way to parse English sentences using robust language technologies [8,11,24]; automatically translating to OW L ([17,4,11]); using templates to facilitate the knowledge entering process [22,24]; maintaining a text-based glossary describing parsed concepts and relationships [26]; and distributing the CL tool as a Protégé plug-in [10]. At the same time, we have tried to minimise the negative usability issues exhibited in existing CL tools, such as reliance on the user having knowledge engineering skills to perform ontology author ing (all existing tools suffer from this to an extent) and lack of immediate feedback and meaningful error messages [10,11,26].

Although the goal of CL tools is to assist in entering knowledge constructs, the existing tools focus solely on the CL aspect - they do not aim to provide assistance for the whole ontology construction process . In this vein, the HALO project 4 makes an important contribution by offering holistic and intuitive support at all stages of ontology authoring [2]. This key design principle is also followed in ROO . HALO focuses on providing advanced functionality based on the state-of-the-art SW technologies, e.g. sophisticated NL parsing of source documents, graphical interface for entering ontology constructs and rule-based queries. In contrast, ROO offers simpler functionality and follows the Ordnance Survey’s practice in ontology construction when taking design decisions. For example, we do not use information extraction techniques to pull out domain concepts from documents, as domain experts normally know what the key concepts are e. Our experience shows that the major challenge is to perform abstraction and to a lesser degree reformulation (from NL to CL) and to formulate ontology constructs in a CL, which is the main focus in ROO. It provides intelligent support for ontology definition by offering proactive guidance based on monitoring domain experts’ activities when performing ontology construction steps. Essentially, certain knowledge engineering expertise has been embedded into ROO to compensate for the lack of such skills in domain experts. This ensures rigour and effectiveness of the ontology development process, and can lead to better quality ontologies (ontology “quality” is described further in §4.3). Furthermore, ROO aims to improve users’ understanding of the knowledge engineering process, and to gradually develop their ontology modelling skills. The st udy presented in this paper is an initial examination of some of these assumptions.

…

5. Discussion and Conclusions

To the best of our knowledge, the study presented here is the first attempt to evaluate how domain experts without knowledge engineering skills can use CL-based tools to complete ontology modelling tasks close to real scenarios (existing studies have either used people with knowledge engineering skills and simple tasks [11] or looked into recognising CL constructs [14]). The results enable us to address key questions concerning the authoring of ontologies where a domain expert takes a central role: Can we use CL to involve domain experts in ontology construction? To what degree can a tool support help the authoring process and substitute for a knowledge engineer? What further support is needed?

…

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2008 InvolvingDomainExpertsinAuthoriVania Dimitrova
Ronald Denaux
Glen Hart
Catherine Dolbear
Ian Holt
Anthony G Cohn		Involving Domain Experts in Authoring OWL Ontologies10.1007/978-3-540-88564-1_12008
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