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"gnparser": a powerful parser for scientific names based on Parsing Expression Grammar.

Dmitry Y Mozzherin1, Alexander A Myltsev2, David J Patterson3

  • 1University of Illinois, Illinois Natural History Survey, Species File Group, 1816 South Oak St., Champaign, 61820, IL, USA. mozzheri@illinois.edu.

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|May 28, 2017
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Summary
This summary is machine-generated.

Global Names Parser (gnparser) is a tool that parses scientific names, improving data exchange in biology. It accurately identifies name elements, enhancing biological data interoperability.

Keywords:
BiodiversityBiodiversity informaticsNames-based cyberinfrastructureParserParsing Expression GrammarScalaScientific nameSemantic parser

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Area of Science:

  • Biodiversity informatics
  • Computational biology
  • Taxonomy

Background:

  • Scientific names are crucial for global biological data referencing.
  • Variations in scientific name spelling (e.g., misspellings, abbreviations) hinder data interconnection.
  • Parsing scientific names into stable and variable elements improves data matching.

Purpose of the Study:

  • To develop a tool for parsing scientific names accurately and efficiently.
  • To address challenges in biological data exchange caused by name variations.

Main Methods:

  • Introduced Global Names Parser (gnparser), a Java tool utilizing Scala and Parsing Expression Grammar.
  • Developed a parser capable of handling complex scientific names, including hybrid names.
  • Implemented a two-stage matching process using stable and variable name elements.

Main Results:

  • gnparser achieves approximately 99% accuracy in parsing scientific names.
  • The tool processes 30 million name-strings per hour per CPU thread.
  • gnparser supports multiple programming languages (Scala, Java, R, Jython, JRuby) and deployment options (command line, web service).

Conclusions:

  • gnparser offers a fast, high-precision solution for parsing large volumes of scientific names.
  • It automates and standardizes scientific name parsing, reducing errors and costs.
  • The tool significantly enhances the interoperability of distributed biological information.