Abstract. Ontology Alignment is the process of identifying semantic correspondences between their entities. It is proposed to enable semantic interoperability between various knowledge sources that are distributed and heterogeneous. Most existing ontology alignment systems are based on the calculation of similarities and often proceed by their combination. The work presented in this paper consists of an approach denoted PBW (Precision Based Weighting) which estimates the weights to assign to matchers for aggregation. This approach proposes to measure the confidence accorded to a matcher by estimating its precision. The experimental study that we have carried out has been conducted on the Conference 1 track of the evaluation campaign OAEI 2 2012. We have compared our approach with two methods considered as the most performed in recent years, namely those based on the concepts harmony and local confidence trust respectively. The results show the good performance of our approach. Indeed, it is better in terms of precision, than existing methods with which it has been compared.

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The amount of sources of information available on the web using ontologies as support continues to increase and is often heterogeneous and distributed. Ontology alignment is the solution to ensure semantic interoperability. In this paper, we describe a new ontology alignment approach, which consists of combining structure-based and reasoning-based approaches in order to discover new semantic correspondences between entities of different ontologies. We used the biblio test of the benchmark series and anatomy series of the Ontology Alignment Evaluation Initiative (OAEI) 2012 evaluation campaign to evaluate the performance of our approach. We compared our approach successively with LogMap and YAM++ systems. We also analyzed the contribution of our method compared to structural and semantic methods. The results obtained show that our performance provides good performance. Indeed, these results are better than those of the LogMap system in terms of precision, recall, and F-measure. Our approach has also been proven to be more relevant than YAM++ for certain types of ontologies and significantly improves the structure-based and reasoningbased methods.

Due to the increasing number of information sources available on the web and their distribution and heterogeneity, ontology alignment became a very important and inevitable problem to resolve in order to ensure semantic interoperability between these sources. Instance-based ontology alignment represents a very promising technique to find semantic correspondences between entities of different ontologies. In practice, two situations may arise: ontologies that share common instances and those share few or do not share common instances. In this paper, the authors describe a new approach to manage the latter case. This approach exploits the reasoning on ontologies in order to create a corpus of common instances. They have used the Biblio and Finance tests of Benchmark series of the OAEI 2012 evaluation campaign to evaluate the performance of their approach. The results obtained show the good performance of the authors' approach compared to ontology alignment systems and improves significantly the instance-based and reasoning-based methods.

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