A Text Mining-Based Framework for Constructing an RDF-Compliant Biodiversity Knowledge Repository
In our aim to make the information encapsulated by biodiversity literature more accessible and searchable, we have developed a text mining-based framework for automatically transforming text into a structured knowledge repository. A text mining workflow employing information extraction techniques, i.e., named entity recognition and relation extraction, was implemented in the Argo platform and was subsequently applied on biodiversity literature to extract structured information. The resulting annotations were stored in a repository following the emerging Open Annotation standard, thus promoting interoperability with external applications. Accessible as a SPARQL endpoint, the repository facilitates knowledge discovery over a huge amount of biodiversity literature by retrieving annotations matching user-specified queries. We present some use cases to illustrate the types of queries that the knowledge repository currently accommodates.