A global bioscience company wanted to standardise its use of scientific terminology. However, they found that most publicly available ontologies focused on pharmaceutical terminology and did not provide appropriate coverage relevant to their business, which is to develop solutions for the food, nutritional and agricultural industries. The manual development and review of a new vocabulary would have consumed significant internal resource.
We started with several existing ontologies, including publicly available bacterial species names, which were enriched using internal terminology commonly used within the client’s organisation, including bacterial strain names and biosafety terms. We also leveraged machine learning techniques to support the enrichment process in a controlled way. For example, to suggest terms that are similar to a word of interest because they are used in a similar context, and to fill gaps not covered by a public ontology.
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Technological advancements exhibit varying degrees of longevity. Some are tried and trusted, enduring longer than others, while other technologies succumb to fleeting hype without attaining substantive fruition. One constant, in this dynamic landscape is the data.
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Public ontologies are essential for applying FAIR principles to data but are not built for use in named entity recognition pipelines. At SciBite, we build on the public ontologies to create VOCabs optimized for NER. In this blog, discover how we create a SciBite VOCab from a Public Ontology.
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