High quality domain ontologies are essential for successful employment of semantic Web services. However, their acquisition is difficult and costly, thus hampering the development of this field. In this talk we report on the first stage of research that aims to develop (semi-)automatic ontology learning tools in the context of Web services that can support domain experts in the ontology building task. The goal of this first stage was to get a better understanding of the problem at hand and to determine which techniques might be feasible to use. To this end, we developed a framework for (semi-)automatic ontology learning from textual sources attached to Web services. The framework exploits the fact that these sources are expressed in a specific sublanguage, making them amenable to automatic analysis. We implement two methods in this framework, which differ in the complexity of the employed linguistic analysis. We evaluate the methods in two different domains, verifying the quality of the extracted ontologies against high quality hand-built ontologies of these domains. Our evaluation lead to a set of valuable conclusions on which further work can be based. First, it appears that our method, while tailored for the Web services context, might be applicable across different domains. Second, we concluded that deeper linguistic analysis is likely to lead to better results. Finally, the evaluation metrics indicate that good results can be achieved using only relatively simple, off the shelf techniques.
Marta Sabou is a Research Fellow at the Knowledge Media Institute (KMi) & Centre for Research and Computing of the Open University. She received a Bachelors Degree in System Engineering from the Technical University of Cluj-Napoca, Romania and a Master's Degree in Artificial Intelligence from the Vrije Universiteit in Amsterdam.
She has completed her PhD at the Knowledge Representation Group of the Vrije Universiteit on the topic of enhancing and (semi-)automatically learning Web service related ontologies. Much of her thesis work has been performed in the context of major European research projects such as WonderWeb, KnowledgeWeb and SWAP. Her research also involved significant cooperation with the UK-founded myGrid project and the OWL-S standardization committee. She has recently been nominated as one of the most promissing young AI reserachers in the context of IEEE's "Ten to Watch" initiative. Her current research focuses on ontology evaluation and selection in the context of two European projects, OpenKnowledge and NeOn. In her spare time she enjoys sporting and learning foreign languages (currently Dutch and Italian).