Abstract. Ontology matching consists of finding correspondences between ontology entities. OAEI campaigns aim at comparing ontology matching systems on precisely defined test sets. Test sets can use ontologies of different nature (from expressive OWL ontologies to simple directories) and use different modalities (e.g., blind evaluation, open evaluation, consensus). OAEI-2007 builds over previous campaigns by having 4 tracks with 7 test sets followed by 18 participants. This is a major increase in the number of participants compared to the previous years. Moreover, the evaluation results demonstrate that more participants are at the forefront. The final and official results of the campaign are those published on the OAEI web site. 1

Abstract. In this paper, we present the AgreementMaker, an ontology alignment tool that incorporates the Descendants Similarity Inheritance (DSI) method. This method uses the structure of the ontology graphs for contextual information, thus providing the matching process with more semantics. We have tested our method on the ontologies included in the anatomy track of the OAEI 2007 campaign. 1 Presentation of the System In distributed database applications with heterogeneous classification schemes that describe related domains, an ontology-driven approach to data sharing and interoperability relies on the alignment of concepts across different ontologies. Once the alignment is established, agreements that encode a variety of mappings between the concepts of the aligned ontologies are derived. In this way, users can potentially query the concepts of a given ontology in terms of other ontologies. To enable scalability both in the size and the number of the ontologies involved, the alignment method should be automatic. In order to achieve this, we have been working on a framework that supports the alignment of two ontologies. In our framework, we introduce an alignment approach that uses

Abstract. With a growing number of ontologies and datasets using those ontologies, ontology mappings become an essential building block of the Semantic Web. In the last years, a larger number of sophisticated ontology matching tools for generating such mappings has been developed. The quality of the mappings provided by those tools typically depends on the settings of the tools ’ parameters. As this is a non-trivial task for an end user, we propose the ECOMatch approach, which asks the user to provide example mappings instead of parameter settings, and automatically determines a suitable parameter setting based on those examples. We show how the preliminary result quality of ontology mappings can be improved by applying automatic, example-based configuration of ontology matching tools. 1

In this abstract we tackle the problem of aligning large ontologies where the mappings suggested by the ontology alignment system need to be validated. Although we focus on an ontology alignment framework, the ideas may be used and extended for community-based or collaborative ontology alignment. In contrast to the case of small ontologies, the computation of mapping suggestions can take a long time and therefore, we would like to be able to start the validation before every mapping suggestion is computed. Further, it is clear that for large ontologies, in general, there are too many mapping suggestions to validate in one time. Therefore, we want a system that allows to partially validate the mapping suggestions and resume the validation later. However, whenever validation decisions have been made, they increase our knowledge about the ontologies and mappings and this knowledge can be used to provide better mapping suggestions. In the remainder of the abstract we propose an iterative ontology alignment framework that deals with these issues. Framework. Our framework is presented in figure 1. The input to the system are the ontologies that need to be aligned, and the output is an alignment between the ontologies.

Abstract. With a growing number of ontologies and datasets using those ontologies, ontology mappings become an essential building block of the Semantic Web. In the last years, a larger number of sophisticated ontology matching tools for generating such mappings has been developed. The quality of the mappings provided by those tools typically depends on the settings of the tools ’ parameters. As this is a non-trivial task for an end user, we propose the ECOMatch approach, which asks the user to provide example mappings instead of parameter settings, and automatically determines a suitable parameter setting based on those examples. We show how the preliminary result quality of ontology mappings can be improved by applying automatic, example-based configuration of ontology matching tools. 1