Unsupervised semantic markup of literature for biodiversity digital libraries

ABSTRACT

This paper reports the further development of machine learning techniques for semantic markup of biodiversity literature, especially morphological descriptions of living organisms such as those hosted at efloras.org and algaebase.org. Syntactic parsing and supervised machine learning techniques have been explored by earlier research. Limitations of these techniques promoted our investigation of an unsupervised learning approach that combines the strength of earlier techniques and avoids the limitations. Semantic markup at the organ and character levels is discussed. Research on semantic markup of natural heritage literature has direct impact on the development of semantic-based access in biodiversity digital libraries.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.7 Digital Libraries
          Subjects: Collection

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.7 Digital Libraries
          Subjects: Systems issues

General Terms:
Algorithms, Design, Experimentation, Performance

Keywords:
biodiversity informatics, morphological description, natural heritage literature, semantic annotation, semantic markup, tagging, unsupervised machine learning, xml