The problem learning Non-Taxonomic Relationships of Ontologies from unstructured data sources

Abstract

In recent years, ontologies as a semantic knowledge representation become widely used in many information systems. Manual creation of ontologies by domain experts and ontology developers is also a costly task, time consuming and needs extra efforts. Learning Non-Taxonomic Relationships is a subfield of ontology learning which targets automatic extraction of non-taxonomic relationships from input, mostly unstructured, data sources and add them into its proper position in the ontology. This paper presents a discussion of the main process of learning Non-Taxonomic Relationships of Ontologies (LNTRO) from unstructured data source as well as corpora and web documents. We addressed the main tasks of LNTRO, the output of each task and techniques used. In addition, a set of state-of-the-art tools for learning non-taxonomic relations are presented. Finally, five approached representing the state of the art of Learning Non-Taxonomic Relationships of Ontologies are described along with their positive and negative aspects.

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Cite this paper

@article{Ali2017ThePL, title={The problem learning Non-Taxonomic Relationships of Ontologies from unstructured data sources}, author={Mohamed S. S. Ali and Said Fathalla and Mohamed Kholief and Yasser F. Hassan}, journal={2017 23rd International Conference on Automation and Computing (ICAC)}, year={2017}, pages={1-6} }