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How ontologies and machine learning work together [Use Case]

How Ontologies and Machine Learning Work Together

The struggle to effectively utilise the increasing volumes of data available is a common challenge in the Life Sciences research industry. Artificial Intelligence (AI) is frequently touted as a potential solution to extract valuable insights from large volumes of heterogeneous data. However, tangible successes to date have been relatively few.

Areas bearing the greatest demonstrable success often utilise machine learning (ML), yet even these are at the mercy of the quality of the source data. Scientifically naive systems struggle to deal with the complexity and variability of unstructured scientific language. In a recent survey of over 16,700 data scientists, the most commonly cited challenge to undertaking machine learning was “dirty data”.

SciBite harmonises data by exploiting ontologies to automate semantic enrichment and annotation, whilst also coping with ambiguities such as synonyms, typographic errors or cryptic data, such as project codes, cell line IDs, and internal drug abbreviations.

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Related articles

  1. Are ontologies relevant in a machine learning-centric world?

    SciBite CSO and Founder Lee Harland shares his views on why ontologies are relevant in a machine learning-centric world and are essential to help "clean up" scientific data in the Life Sciences industry.

  2. The benefits of centralizing ontology management

    Ontologies have become a key piece of infrastructure for organisations as they look to manage their metadata to improve the reusability and findability of their data. This is the first blog in our blog series 'Ontologies with SciBite'. Follow the blog series to learn how we've addressed the challenges associated with both consuming and developing ontologies.


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