An efficient concept-based retrieval model for enhancing text retrieval quality View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-05

AUTHORS

Shady Shehata, Fakhri Karray, Mohamed S. Kamel

ABSTRACT

Most of the common techniques in text retrieval are based on the statistical analysis terms (words or phrases). Statistical analysis of term frequency captures the importance of the term within a document only. Thus, to achieve a more accurate analysis, the underlying model should indicate terms that capture the semantics of text. In this case, the model can capture terms that represent the concepts of the sentence, which leads to discovering the topic of the document. In this paper, a new concept-based retrieval model is introduced. The proposed concept-based retrieval model consists of conceptual ontological graph (COG) representation and concept-based weighting scheme. The COG representation captures the semantic structure of each term within a sentence. Then, all the terms are placed in the COG representation according to their contribution to the meaning of the sentence. The concept-based weighting analyzes terms at the sentence and document levels. This is different from the classical approach of analyzing terms at the document level only. The weighted terms are then ranked, and the top concepts are used to build a concept-based document index for text retrieval. The concept-based retrieval model can effectively discriminate between unimportant terms with respect to sentence semantics and terms which represent the concepts that capture the sentence meaning. Experiments using the proposed concept-based retrieval model on different data sets in text retrieval are conducted. The experiments provide comparison between traditional approaches and the concept-based retrieval model obtained by the combined approach of the conceptual ontological graph and the concept-based weighting scheme. The evaluation of results is performed using three quality measures, the preference measure (bpref), precision at 10 documents retrieved (P(10)) and the mean uninterpolated average precision (MAP). All of these quality measures are improved when the newly developed concept-based retrieval model is used, confirming that such model enhances the quality of text retrieval. More... »

PAGES

411-434

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10115-012-0504-y

DOI

http://dx.doi.org/10.1007/s10115-012-0504-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1028586303


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