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ABLE Project

Automated Biodiversity Literature Enhancement


Biological knowledge, especially taxonomic knowledge, is often presented in a stylised form, generally using typographical clues as to its meaning. This project aims to use typographical information and other contextual clues to identify and tag document content by type. This combination of Natural Language Processing (NLP) with typographical information extraction should be applicable in other fields that historically use structured data.

Aims and Objectives

The project seeks to enhance access to a large body of scanned literature in the biodiversity domain by developing fuzzy matching of search terms, so that searching the literature is robust to errors introduced by OCR and other sources. The primary goal of the project is structural recognition, disambiguation and mark-up, from which metadata (taxon names, people's names, locations and dates) will be extracted to build indices and ontologies from the rapidly growing digital content of the Biodiversity Heritage Library.