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Scientific Programming
Volume 2017 (2017), Article ID 7831897, 10 pages
https://doi.org/10.1155/2017/7831897
Research Article

Semantic Annotation of Unstructured Documents Using Concepts Similarity

1National Center of Research and Technological Development (CENIDET), Cuernavaca, MOR, Mexico
2Center for Research and Innovation in Information and Communications Technologies, Ciudad de México, Mexico
3National Institute of Electricity and Clean Energy (INEEL), Cuernavaca, MOR, Mexico

Correspondence should be addressed to Fernando Pech

Received 17 June 2017; Revised 2 October 2017; Accepted 8 November 2017; Published 7 December 2017

Academic Editor: José María Álvarez-Rodríguez

Copyright © 2017 Fernando Pech et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

There is a large amount of information in the form of unstructured documents which pose challenges in the information storage, search, and retrieval. This situation has given rise to several information search approaches. Some proposals take into account the contextual meaning of the terms specified in the query. Semantic annotation technique can help to retrieve and extract information in unstructured documents. We propose a semantic annotation strategy for unstructured documents as part of a semantic search engine. In this proposal, ontologies are used to determine the context of the entities specified in the query. Our strategy for extracting the context is focused on concepts similarity. Each relevant term of the document is associated with an instance in the ontology. The similarity between each of the explicit relationships is measured through the combination of two types of associations: the association between each pair of concepts and the calculation of the weight of the relationships.