This paper presents an approach to automating semantic annotation within service-oriented architectures that provide interfaces to databases of spatialinformation objects. The automation of the annotation process facilitates the transition from the current state-of-the-art architectures towards semantically-enabled architectures. We see the annotation process as the task of matching an arbitrary word or term with the most appropriate concept in the domain ontology. The term matching techniques that we present are based on text mining. To determine the similarity between two terms, we first associate a set of documents [that we obtain from a Web search engine] with each term. We then transform the documents into feature vectors and thus transition the similarity assessment into the feature space. After that, we compute the similarity by training a classifier to distinguish between ontology concepts. Apart from text mining approaches, we also present an alternative technique, namely Google Distance, which proves less suitable for our task. The paper also presents the results of an extensive evaluation of the presented term matching methodswhich shows that these methodswork best on synonymous nouns from a specific vocabulary. Furthermore, the fast and simple centroid-based classifier is shown to perform very well for this task. The main contribution of this paper is thus in proposing a term matching algorithm based on text mining and information retrieval. Furthermore, the presented evaluation should give a notion of how the algorithm performs in various scenarios.
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