Creation of Individual Scientific Concept-Centered Semantic Maps Based on Automated Text-Mining Analysis of PubMed | Zendy

Ekaterina V. Ilgisonis | Zendy; Andrey Lisitsa | Zendy; Valerya Kudryavtseva | Zendy; Elena A. Ponomarenko | Zendy

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Creation of Individual Scientific Concept-Centered Semantic Maps Based on Automated Text-Mining Analysis of PubMed

Author(s) -

Ekaterina V. Ilgisonis,

Andrey Lisitsa,

Valerya Kudryavtseva,

Elena A. Ponomarenko

Publication year - 2018

Publication title -

advances in bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.33

H-Index - 20

eISSN - 1687-8035

pISSN - 1687-8027

DOI - 10.1155/2018/4625394

Subject(s) - computer science , information retrieval , text mining , data science , semantic analysis (machine learning) , data mining

Concept-centered semantic maps were created based on a text-mining analysis of PubMed using the BiblioEngine_v2018 software. The objects (“concepts”) of a semantic map can be MeSH-terms or other terms (names of proteins, diseases, chemical compounds, etc.) structured in the form of controlled vocabularies. The edges between the two objects were automatically calculated based on the index of semantic similarity, which is proportional to the number of publications related to both objects simultaneously. On the one hand, an individual semantic map created based on the already published papers allows us to trace scientific inquiry. On the other hand, a prospective analysis based on the study of PubMed search history enables us to determine the possible directions for future research.

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