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Experimental data for computing semantic similarity between concepts using multiple inheritances in Wikipedia category graph.

ABSTRACT: This data article compiles the detailed and descriptive experimental data of Wikipedia-based semantic similarity approach called as Neighbourhood Aggregated Semantic Contribution (NASC), presented in Husain, et al. [1]. The JWPL (Java Wikipedia Library)-DataMachine and JWPL WikipediaAPI are used to extract the required Wikipedia features from Wikipedia dump. The dataset presents the disambiguated Wikipedia concepts of the gold standard word similarity benchmarks MC30 (English), RG65es (Spanish) and RG65fr (French) and their associated set of categories in the corresponding Wikipedia category graph (WCG). The dataset also contains the number of ancestors, common ancestors, pages, and common pages in the k-neighbourhood of the associated categories for different levels of parameter k in the English, Spanish, and French WCGs. The presented dataset can be used to assess the semantic similarity between Wikipedia concepts in English (MC30), Spanish (RG65es), and French (RG65fr) languages benchmarks. Moreover, the dataset will be useful for the further analysis and comparison of the taxonomic structures of the English, Spanish, and French WCGs.

SUBMITTER: Hussain MJ

PROVIDER: S-EPMC7118305 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Experimental data for computing semantic similarity between concepts using multiple inheritances in Wikipedia category graph.

Hussain Muhammad Jawad MJ Wasti Shahbaz Hassan SH Huang Guangjian G Jiang Yuncheng Y

Data in brief 20200310

This data article compiles the detailed and descriptive experimental data of Wikipedia-based semantic similarity approach called as Neighbourhood Aggregated Semantic Contribution (NASC), presented in Husain, et al. [1]. The JWPL (Java Wikipedia Library)-DataMachine and JWPL WikipediaAPI are used to extract the required Wikipedia features from Wikipedia dump. The dataset presents the disambiguated Wikipedia concepts of the gold standard word similarity benchmarks MC30 (English), RG65<sub>es</sub> ...[more]

PMID: 32258267

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Project description:Biomedical semantic indexing is a very useful support tool for human curators in their efforts for indexing and cataloging the biomedical literature.The aim of this study was to describe a system to automatically assign Medical Subject Headings (MeSH) to biomedical articles from MEDLINE.Our approach relies on the assumption that similar documents should be classified by similar MeSH terms. Although previous work has already exploited the document similarity by using a k-nearest neighbors algorithm, we represent documents as document vectors by search engine indexing and then compute the similarity between documents using cosine similarity. Once the most similar documents for a given input document are retrieved, we rank their MeSH terms to choose the most suitable set for the input document. To do this, we define a scoring function that takes into account the frequency of the term into the set of retrieved documents and the similarity between the input document and each retrieved document. In addition, we implement guidelines proposed by human curators to annotate MEDLINE articles; in particular, the heuristic that says if 3 MeSH terms are proposed to classify an article and they share the same ancestor, they should be replaced by this ancestor. The representation of the MeSH thesaurus as a graph database allows us to employ graph search algorithms to quickly and easily capture hierarchical relationships such as the lowest common ancestor between terms.Our experiments show promising results with an F1 of 69% on the test dataset.To the best of our knowledge, this is the first work that combines search and graph database technologies for the task of biomedical semantic indexing. Due to its horizontal scalability, ElasticSearch becomes a real solution to index large collections of documents (such as the bibliographic database MEDLINE). Moreover, the use of graph search algorithms for accessing MeSH information could provide a support tool for cataloging MEDLINE abstracts in real time.

Dataset Information

Experimental data for computing semantic similarity between concepts using multiple inheritances in Wikipedia category graph.

Publications

Experimental data for computing semantic similarity between concepts using multiple inheritances in Wikipedia category graph.

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