Learning ontology alignments using recursive neural networks

Authors:
Alexandros Chortaras;Giorgos Stamou;Andreas Stafylopatis
Affiliations:
School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

Citing 3
Cited 3

PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Supervised neural networks for the classification of structures

IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures

IEEE Transactions on Neural Networks

ANN-agent for distributed knowledge source discovery

OTM'07 Proceedings of the 2007 OTM confederated international conference on On the move to meaningful internet systems - Volume Part I
Knowledge discovery through ontology matching: An approach based on an Artificial Neural Network model

Information Sciences: an International Journal
Ontology alignment using artificial neural network for large-scale ontologies

International Journal of Metadata, Semantics and Ontologies

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.