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Ontology Alignment in Life Sciences

Edited by: Ernesto Jiménez-Ruiz, Daniel Faria, and Pavel Shvaiko

Ontology alignment is a key interoperability enabler for the Semantic Web. It takes ontologies as input and determines as output an alignment, that is, a set of correspondences between the semantically related entities of those ontologies. Matching ontologies enables the knowledge and data expressed with the matched ontologies to interoperate. The Ontology Matching (OM) workshop ( is held annualy and aims at bringing together leaders from academia, industry and user institutions to assess how academic advances are addressing real-world requirements. The OM workshop also conducts an extensive and rigorous evaluation of ontology matching and link discovery approaches through the OAEI (Ontology Alignment Evaluation Initiative, The OAEI has played a key role in the benchmarking ontology matching systems by facilitating (i) their comparison on the same basis, and (ii) the reproducibility of the evaluation and results. The OAEI includes different tracks organised by different research groups. Each track contains one or more matching tasks involving small-size (e.g., conference), medium-size (e.g., anatomy), large (e.g., phenotype) or very large (e.g., largebio) ontologies.

This collections gathered (i) relevant papers to Life Sciences submitted to the Ontology Matching 2016 workshop, (ii) system papers with competitive results in the OAEI 2016 biomedical-themed tracks (anatomy, largebio and phenotype), and (iii) biomedical-themed dataset descriptions to benchmark ontology alignment systems.

  1. The goal of ontology matching is to identify correspondences between entities from different yet overlapping ontologies so as to facilitate semantic integration, reuse and interoperability. As a well developed...

    Authors: Mengyi Zhao, Songmao Zhang, Weizhuo Li and Guowei Chen
    Citation: Journal of Biomedical Semantics 2018 9:11
  2. Biomedical ontologies pose several challenges to ontology matching due both to the complexity of the biomedical domain and to the characteristics of the ontologies themselves. The biomedical tracks in the Onto...

    Authors: Daniel Faria, Catia Pesquita, Isabela Mott, Catarina Martins, Francisco M. Couto and Isabel F. Cruz
    Citation: Journal of Biomedical Semantics 2018 9:4
  3. Integration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomica...

    Authors: Miguel Ángel Rodríguez-García, Georgios V. Gkoutos, Paul N. Schofield and Robert Hoehndorf
    Citation: Journal of Biomedical Semantics 2017 8:58
  4. The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important f...

    Authors: Ian Harrow, Ernesto Jiménez-Ruiz, Andrea Splendiani, Martin Romacker, Peter Woollard, Scott Markel, Yasmin Alam-Faruque, Martin Koch, James Malone and Arild Waaler
    Citation: Journal of Biomedical Semantics 2017 8:55

Annual Journal Metrics

  • 2022 Citation Impact
    1.9 - 2-year Impact Factor
    2.6 - 5-year Impact Factor
    0.870 - SNIP (Source Normalized Impact per Paper)
    0.697 - SJR (SCImago Journal Rank)

    2023 Speed
    23 days submission to first editorial decision for all manuscripts (Median)
    265 days submission to accept (Median)

    2023 Usage 
    206 Altmetric mentions