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Abstract

Classification schemes, such as the DMoZ web directory, provide a convenient and intuitive way for humans to access classified contents. While being easy to be dealt with for humans, classification schemes remain hard to be reasoned about by automated software agents. Among other things, this hardness is conditioned by the ambiguous na- ture of the natural language used to describe classification categories. In this paper we describe how classification schemes can be converted into OWL ontologies, thus enabling reasoning on them by Semantic Web applications. The proposed solution is based on a two phase approach in which category names are first encoded in a concept language and then, together with the structure of the classification scheme, are converted into an OWL ontology. We demonstrate the practical applicability of our approach by showing how the results of reasoning on these OWL ontologies can help improve the organization and use of web directories.

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Abstract

In this document we provide an overall view of the state of the art in ontology alignment. It is organised as a description of the need for ontology alignment, a presentation of the techniques currently in use for ontology alignment and a presentation of existing systems. The state of the art is not restricted to any discipline and consider as some form of ontology alignment the work made on schema matching within the database area for instance. Heterogeneity problems on the semantic web can be solved, for some of them, by aligning heterogeneous ontologies. This is illustrated through a number of use cases of ontology alignment. Aligning ontologies consists of providing the corresponding entities in these ontologies. This process is precisely defined in deliverable D2.2.1. The current deliverable presents the many techniques currently used for implementing this process. These techniques are classified along the many features that can be found in ontologies (labels, structures, instances, semantics). They resort to many different disciplines such as statistics, machine learning or data analysis. The alignment itself is obtained by combining these techniques towards a particular goal (obtaining an alignment with particular features, optimising some criterion). Several combination techniques are also presented. Finally, these techniques have been experimented in various systems for ontology alignment or schema matching. Several such systems are presented briefly in the last section and characterized by the above techniques they rely on. The conclusion is that many techniques are available for achieving ontology alignment and many systems have been developed based on these techniques. However, few comparisons and few integration is actually provided by these implementations. This deliverable serves as a basis for considering further action along these two lines. It provide a first inventory of what should be evaluated and suggests what evaluation criterion can be used.