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Ontology management: A simple, collaborative and robust approach [Whitepaper]

Ontology management

Ontologies play a critical role in semantic enrichment, enabling unstructured scientific text to be transformed into clean, contextualised data which can be understood and exploited by computational approaches, such as machine learning. However, the process of managing these ontologies presents a challenge to most Life Sciences organisations. Maintaining multiple, evolving ontologies from both public and proprietary sources, requires significant effort from ontology experts.

SciBite’s mission is to simplify both the management and use of ontologies. In our vision of a world with effective ontology management, there would be a centrally available source of information for all users, regardless of their particular use case or application. Ontologies would blend both public and internal terminology to facilitate their use across multiple different groups. Scientists would become an integral part of the process by requesting updates to ontologies. All editing would be in a purpose-built user interface with no extraneous or unnecessarily complex functionality. Plus there would be a fully traceable log to manage evolving versions of an ontology.

In this whitepaper, we expand upon this vision and describe SciBite’s integrated approach to simple, collaborative and robust ontology management.

To find out more, download the full whitepaper.

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  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. Exploring ontology visualization techniques for biological data

    What’s the most useful way to visualize an ontology? SciBite CTO gives his views on answering this commonly asked question regarding ontology visualization techniques.


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