USI: a fast and accurate approach for conceptual document annotation

BMC Bioinformatics

Nicolas Fiorini²  Sylvie Ranwez²  Jacky Montmain²  Vincent Ranwez¹ 

[1] UMR AGAP, Montpellier SupAgro/CIRAD/INRA, 2 place Pierre Viala, Montpellier Cedex 1 34060, France

[2] LGI2P research center from the Ecole des mines d’Alès, Site de Nîmes, Parc scientifique G. Besse, Nîmes cedex 1 30035, France

关键词: Benchmarking;    Complexity;    Algorithms;    Semantic annotations;   

Others  :  1139038
DOI  :  10.1186/s12859-015-0513-4

 received in 2014-10-23, accepted in 2015-02-24,  发布年份 2015

【 摘 要 】

Background

Semantic approaches such as concept-based information retrieval rely on a corpus in which resources are indexed by concepts belonging to a domain ontology. In order to keep such applications up-to-date, new entities need to be frequently annotated to enrich the corpus. However, this task is time-consuming and requires a high-level of expertise in both the domain and the related ontology. Different strategies have thus been proposed to ease this indexing process, each one taking advantage from the features of the document.

Results

In this paper we present USI (User-oriented Semantic Indexer), a fast and intuitive method for indexing tasks. We introduce a solution to suggest a conceptual annotation for new entities based on related already indexed documents. Our results, compared to those obtained by previous authors using the MeSH thesaurus and a dataset of biomedical papers, show that the method surpasses text-specific methods in terms of both quality and speed. Evaluations are done via usual metrics and semantic similarity.

Conclusions

By only relying on neighbor documents, the User-oriented Semantic Indexer does not need a representative learning set. Yet, it provides better results than the other approaches by giving a consistent annotation scored with a global criterion — instead of one score per concept.

【 授权许可】

   
2015 Fiorini et al.; licensee BioMed Central.

【 图 表 】

Figure 1.

Figure 2.

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