Use this URL to cite or link to this record in EThOS:
Title: Intuitive ontology authoring using controlled natural language
Author: Denaux, Ronald
ISNI:       0000 0004 2744 2765
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2013
Availability of Full Text:
Access from EThOS:
Access from Institution:
Ontologies have been proposed and studied in the last couple of decades as a way to capture and share people's knowledge about the world in a way that is processable by computer systems. Ontologies have the potential to serve as a bridge between the human conceptual understanding of the world and the data produced, processed and stored in computer systems. However, ontologies so far have failed to gather widespread adoption, failing to realise the original vision of the semantic web as a next generation of the world wide web: where everyone would be able to contribute and interlink their data and knowledge as easily as they can contribute and interlink their websites. One of the main reasons for this lack of widespread adoption of ontologies is the steep learning curve for authoring them: most people find it too dfficult to learn the syntax and formal semantics of ontology languages. Most research has tried to alleviate this problem by finding ways to help people to collaborate with knowledge engineers when building ontologies; this approach however, requires the wide availability of knowledge engineers, who in practice are scarce. In the context of the semantic web, recent research has started looking at ways to directly capture knowledge from domain experts as ontologies. One such approach advocates the use of Controlled Natural Languages (CNL) as a promising way to alleviate the syntactical impediment to writing ontological constructs. However, not much is yet known about the capabilities and limitations of CNL-based ontology authoring by domain experts. It is also unknown what type of automatic tool support can and should be provided to novice ontology authors, although such intelligent tool support is becoming possible due to advances in reasoning with existing ontologies and other related areas such as natural language processing. This PhD investigates how CNL-based ontology authoring systems can make ontology authoring more accessible to domain experts by providing intelligent tool support. In particular, this thesis iteratively investigates the impact of providing various types of intelligent tool support for authoring ontologies using the Web Ontology Language (OWL) and a controlled natural language called Rabbit. After each iteration of added tool support, we evaluate how it impacts the ontology authoring process and what are the main limitations of the resulting ontology authoring system. Based on the found limitations, we decide which further tool support would be most beneficial to novice ontology authors. This methodology resulted in iteratively providing support for (i) understanding the syntactic capabilities and limitations of the chosen controlled natural language; (ii) following appropriate ontology engineering methodologies; (iii) fostering awareness about the logical consequences of adding new knowledge to an ontology and (iv) interacting with the ontology authoring system via dialogues. The main contributions of this PhD are (i) showing that domain experts benefit from guidance about the ontology authoring process and understandable syntax error messages for finding the correct CNL syntax; (ii) the definition of a framework to integrate the syntactical and semantic analyses of ontology authors' inputs; (iii) showing that intuitive feedback about the integration of ontology authors' inputs into an existing ontology benefits ontology authors as they become aware of potential ontology defects; (iv) the definition of a framework to analyse and describe ontology authoring in terms of dialogue moves and their discourse structure.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available