title: Semantic annotation of morphological descriptions: an overall strategy.
abstract: BACKGROUND
  Large volumes of morphological descriptions of whole organisms have been created as print or electronic text in a human-readable format. Converting the descriptions into computer- readable formats gives a new life to the valuable knowledge on biodiversity. Research in this area started 20 years ago, yet not sufficient progress has been made to produce an automated system that requires only minimal human intervention but works on descriptions of various plant and animal groups. This paper attempts to examine the hindering factors by identifying the mismatches between existing research and the characteristics of morphological descriptions.
  
  
  RESULTS
  This paper reviews the techniques that have been used for automated annotation, reports exploratory results on characteristics of morphological descriptions as a genre, and identifies challenges facing automated annotation systems. Based on these criteria, the paper proposes an overall strategy for converting descriptions of various taxon groups with the least human effort.
  
  
  CONCLUSIONS
  A combined unsupervised and supervised machine learning strategy is needed to construct domain ontologies and lexicons and to ultimately achieve automated semantic annotation of morphological descriptions. Further, we suggest that each effort in creating a new description or annotating an individual description collection should be shared and contribute to the "biodiversity information commons" for the Semantic Web. This cannot be done without a sound strategy and a close partnership between and among information scientists and biologists.
authors.affiliation: School of Information Resources and Library Science, University of Arizona, 1515 E, First Street, Tucson, Arizona 85719, USA. hongcui@email.arizona.edu
authors.forename: Hong
authors.initials: H
authors.lastname: Cui
mesh_terms: D001185:Artificial Intelligence; D044822:Biodiversity; D003628:Database Management Systems; D009323:Natural Language Processing; D012660:Semantics