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.
Historically, maintaining multiple, evolving ontologies from both public and proprietary sources, has been a complex undertaking, requiring a high degree of ontology expertise. This presents a bottleneck for most organisations and undermines the concept that an ontology should represent a shared understanding between experts.
CENtree provides a centralised, enterprise-ready resource for ontology management and transforms the experience of maintaining and releasing ontologies for research-led businesses. CENtree combines ease of use with cutting-edge artificial intelligence techniques to assist users, for example, by suggesting possible relationship connections for a given ontology class.
Get in touch with the team to learn more or download the CENtree datasheet.
Democratises ontology editing so that ontology users can easily contribute, rather than rely solely on ontology experts, as well as browse and search the platform
Centralises and controls the process of consuming and editing external and internal ontology resources over time
Rich API simplifies integration with search and data capture applications, transforming the way you manage internal data to empower downstream insights through data mining and machine learning
Learn more about CENtree, our collaborative ontology management platform tailored to life sciences.Download
Cambridge, UK - SciBite, the award-winning semantic software company, today announced the release of CENtree 1.0, the latest version of their innovative, collaborative platform which revolutionises the way Life Sciences organisations manage and release ontologies.Read
SciBite's CTO James Malone explains how the semantic approach to using ontologies is essential in successfully training machine learning data sets. In this blog he discusses how Sherlock Holmes (amongst others) made an appearance when we looked to exploit the efforts of Wikipedia to identify articles relevant to the life science domain for a language model project.Read
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