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DL-VSM based document indexing approach for information retrieval

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Abstract

Textual information is constantly increasing. With this accumulation of documents, the satisfaction of user needs becomes more and more complex. For that, several information retrieval systems have been designed in order to respond to user requests. Document indexing is considered as a crucial phase in the information retrieval field. The main contribution of the current work resides in the suggestion of a novel hybrid approach for biomedical document indexing. We improve the estimation of the correspondence between a document and a given concept using two methods: vector space model (VSM) and description logics (DL). VSM performs partial matching between documents and external resource terms. DL allows representing knowledge in a relevant manner for better matching. The proposed contribution reduces the limitation of exact matching. It serves to index documents by exploiting medical subject headings (MeSH) thesaurus services with approximate matching. The latter partially matches document terms with biomedical vocabularies to extract other morphological variants in that resource. It also generates irrelevant concepts. The filtering step solves this problem and grants the selection of the most important concepts by exploiting the knowledge provided by MeSH. The experiments, carried out on different corpora, show encouraging results (+ 25% improvement in average accuracy compared to other approaches in the literature).

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Fig. 1

Notes

  1. CNLP.Platform http://www.nlp.org.cn.

  2. https://www.nlm.nih.gov/research/umls/.

  3. This research work is supported in part from the NSF Career Grant (NSF IIS 0448023). NSF CCF 0514679 and the research Grant from PA Dept of Health.

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  1. MARS Research Laboratory, University of Sousse, Sousse, Tunisia

    Kabil Boukhari & Mohamed Nazih Omri

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  1. Kabil Boukhari
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  2. Mohamed Nazih Omri
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Correspondence to Kabil Boukhari.

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Boukhari, K., Omri, M.N. DL-VSM based document indexing approach for information retrieval. J Ambient Intell Human Comput 14, 5383–5394 (2023). https://doi.org/10.1007/s12652-020-01684-x

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