Introducing Artificial Neural Network in Ontologies Alignement Process

  • Warith Eddine Djeddi
  • Mohamed Tarek Khadir
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 185)


During automated/semi-automated alignment across myriad ontologies, different similarity measures of different categories such as string, linguistic, and structural based similarity measures, contribute each to some extend to alignment results. A weights vector must, therefore, be assigned to these similarity measures, if a more accurate and meaningful alignment result is favored. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics and/or prior domain knowledge. In this paper, we take an artificial neural network approach to learn and adjust these weights, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. XMap++ is applied to benchmark tests at OAEI campaign 2010. Results show that neural network boosts the performance in most cases, and that the proposed novel approach is competitive with top-ranked system.


Neural Network Benchmark Test Semantic Aspect Ontology Match Ontology Alignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.LabGED, Computer Science DepartmentUniversity Badji MokhtarAnnabaAlgeria

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