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Centralized Resource for Development, Testing, Evaluation and Comparison of BioNLP Text Mining Systems in Domain of Mutations
Background: Experimental research on the automatic extraction of information about mutations from texts is greatly hindered by the lack of consensus evaluation facilities and easy-to-use infrastructure for the testing and benchmarking of mutation text mining systems.
Work: We propose a community-oriented annotation and benchmarking infrastructure to support development, testing, benchmarking, and comparison of mutation text mining systems. The design is based on semantic standards, where RDF is used to represent the annotations, an OWL ontology provides an extensible schema for the data and SPARQL is used to compute various performance metrics, so that in many cases no programming is needed to analyze results from a text-mining system. While large benchmark corpora for biological entity an
