Our Work
Data Classification

University of Leicester Library

How Nebuli applied augmented intelligence to unearth University of Leicester's deep data's hidden world of knowledge within their research papers.

Nebuli is independent from any data formats, platforms and languages — allowing easier and faster cross-disciplinary and cross-regional data integrations and interoperability. This facilitates the discovery of hidden knowledge and undiscovered trends within given documents, such as research papers, Excel files and more.

Nebuli helped the UoL’s library to visualise the hidden world of their internal research papers, facilitating new interdisciplinary and interdepartmental R&D collaborations.

Nebuli also helped the university to build their own data indices within Nebuli itself from their own documents and datasets, without touching or modifying their original data sources. In addition, this was combined with downloaded libraries from PubMed and are currently working on indexing further open online libraries, including Google Scholar and archive.org.

Nebuli’s Key Role

  • Data preprocessing (cleansing) of the library’s unlabelled database.
  • Data segmentation, with particular focus on identifying communities within the same database. This can be used to find researchers with similar research interests, including researchers working in closely related fields (as well as seemingly unrelated fields) in different department.
  • Not only does Nebuli search for similar documents, it goes beyond by generating a unique measure of similarity.
  • Providing users with tools that evaluate the quality of the groups, generating keywords that represent the content of each group.
  • Returning similar documents not only based on keywords, but also based on the documents’ full text content.


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University of Leicester

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