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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Learning Ontology Alignments Using Recursive Neural Networks
verfasst von : Alexandros Chortaras, Giorgos Stamou, Andreas Stafylopatis
Erschienen in: Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005
Verlag: Springer Berlin Heidelberg
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The Semantic Web is based on technologies that make the content of the Web machine-understandable. In that framework, ontological knowledge representation has become an important tool for the analysis and understanding of multimedia information. Because of the distributed nature of the Semantic Web however, ontologies describing similar fields of knowledge are being developed and the data coming from similar but non-identical ontologies can be combined only if a semantic mapping between them is first established. This has lead to the development of several ontology alignment tools. We propose an automatic ontology alignment method based on the recursive neural network model that uses ontology instances to learn similarities between ontology concepts. Recursive neural networks are an extension of common neural networks, designed to process efficiently structured data. Since ontologies are a structured data representation, the model is inherently suitable for use with ontologies.