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2019 | OriginalPaper | Chapter

Ontologies for Data Science: On Its Application to Data Pipelines

Authors : Miguel-Ángel Sicilia, Elena García-Barriocanal, Salvador Sánchez-Alonso, Marçal Mora-Cantallops, Juan-José Cuadrado

Published in: Metadata and Semantic Research

Publisher: Springer International Publishing

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Abstract

Ontologies are usually applied to drive intelligent applications and also as a resource for integrating or extracting information, as in the case of Natural Language Processing (NLP) tasks. Further, ontologies as the Gene Ontology (GO) are used as an artifact for very specific research aims. However, the value of ontologies for data analysis tasks may also go beyond these uses and span supporting the reuse and composition of data acquisition, integration and fusion code. This requires that both data and code artifacts support meta-descriptions using shared conceptualizations. In this paper, we discuss the different concerns in semantically describing data pipelines as a key reusable artifact that could be retrieved, compared and reused with a degree of automation if semantically consistent descriptions are provided. Concretely, we propose attaching semantic descriptions for data and analytic transformations to current backend-independent distributed processing frameworks as Apache Beam, as these already abstract out the specificity of supporting execution engines.

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previous chapter GLOBDEF: A Framework for Dynamic Pipelines of Semantic Data Enrichment Tools

next chapter Relating Legal Entities via Open Information Extraction

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At the time of this writing, Bean is mainly used as a data transformation framework not including distributed machine learning algorithms, for example, TensorFlow Extended (TFX) [4] is built on top of Beam but there are not algorithms available as aggregate transformations.

The transformations are just examples, they are not intended as analytics with real practical value.

This would require integrating a parser of that language for checking and manipulation, that could include exploiting semantic mappings.

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Title: Ontologies for Data Science: On Its Application to Data Pipelines
Authors: Miguel-Ángel Sicilia
Elena García-Barriocanal
Salvador Sánchez-Alonso
Marçal Mora-Cantallops
Juan-José Cuadrado
Publisher: Springer International Publishing
Book: Metadata and Semantic Research
Print ISBN: 978-3-030-14400-5

Electronic ISBN: 978-3-030-14401-2

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-14401-2_16

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