SIENA: Semi-automatic semantic enhancement of datasets using concept recognition

Andreea Grigoriu*, Amrapali Zaveri, Gerhard Weiss, Michel Dumontier

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Background The amount of available data, which can facilitate answering scientific research questions, is growing. However, the different formats of published data are expanding as well, creating a serious challenge when multiple datasets need to be integrated for answering a question. Results This paper presents a semi-automated framework that provides semantic enhancement of biomedical data, specifically gene datasets. The framework involved a concept recognition task using machine learning, in combination with the BioPortal annotator. Compared to using methods which require only the BioPortal annotator for semantic enhancement, the proposed framework achieves the highest results. Conclusions Using concept recognition combined with machine learning techniques and annotation with a biomedical ontology, the proposed framework can provide datasets to reach their full potential of providing meaningful information, which can answer scientific research questions.
Original languageEnglish
Article number5
Number of pages12
JournalJournal of biomedical semantics
Issue number1
Publication statusPublished - 24 Mar 2021


  • Ontology
  • Semantic enhancement
  • Gene
  • Deep learning
  • Machine learning

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