An integrated platform supporting semantic similarity score calculation and reproducibility. Mazandu, G. K, Opap, K., Agamah, F., Bope, C., Chimusa, E. R, Wonkam, A., & Mulder, N. J Research Square, aug, 2021.
An integrated platform supporting semantic similarity score calculation and reproducibility [link]Paper  doi  abstract   bibtex   
During the last decade, we witnessed an exponential rise of datasets from heterogeneous sources. Ontologies are playing an essential role in consistently describing domain concepts, data harmonization and integration to support large-scale integrative analysis and semantic interoperability in knowledge sharing. Several semantic similarity (SS) measures have been suggested to enable the integration of rich ontology structures into automated reasoning and inference. However, there is no tool that exhaustively implements these measures and existing tools are generally Gene Ontology specic, do not implement several models suggested in the WordNet context and are not equipped to properly deal with frequent ontology updates. We introduce a Python SS measure library (PySML), which tackles issues related to current SS tools, providing a portable and expandable tool to a broad computational audience. This empowers users to manipulate SS scores from several applications for any ontology version and le format. PySML is a exible tool enabling the implementation of all existing semantic similarity models, resolving issues related to computation, reproducibility and re-usability of SS scores.
@article{Mazandu2021,
abstract = {During the last decade, we witnessed an exponential rise of datasets from heterogeneous sources. Ontologies are playing an essential role in consistently describing domain concepts, data harmonization and integration to support large-scale integrative analysis and semantic interoperability in knowledge sharing. Several semantic similarity (SS) measures have been suggested to enable the integration of rich ontology structures into automated reasoning and inference. However, there is no tool that exhaustively implements these measures and existing tools are generally Gene Ontology specic, do not implement several models suggested in the WordNet context and are not equipped to properly deal with frequent ontology updates. We introduce a Python SS measure library (PySML), which tackles issues related to current SS tools, providing a portable and expandable tool to a broad computational audience. This empowers users to manipulate SS scores from several applications for any ontology version and le format. PySML is a exible tool enabling the implementation of all existing semantic similarity models, resolving issues related to computation, reproducibility and re-usability of SS scores.},
annote = {Under review at Scientific Reports, 24 August 2021},
author = {Mazandu, Gaston K and Opap, Kenneth and Agamah, Francis and Bope, Christian and Chimusa, Emile R and Wonkam, Ambroise and Mulder, Nicola J},
doi = {10.21203/RS.3.RS-806346/V1},
file = {:C$\backslash$:/Users/01462563/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mazandu et al. - 2021 - An integrated platform supporting semantic similarity score calculation and reproducibility.pdf:pdf},
journal = {Research Square},
keywords = {OA,Python SS measure library (PySML),fund{\_}not{\_}ack,original,semantic similarity (SS)},
mendeley-tags = {OA,fund{\_}not{\_}ack,original},
month = {aug},
title = {{An integrated platform supporting semantic similarity score calculation and reproducibility}},
url = {https://www.researchsquare.com https://www.researchsquare.com/article/rs-806346/v1},
year = {2021}
}

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