title: Mapping biological entities using the longest approximately common prefix method.
abstract: BACKGROUND
  The significant growth in the volume of electronic biomedical data in recent decades has pointed to the need for approximate string matching algorithms that can expedite tasks such as named entity recognition, duplicate detection, terminology integration, and spelling correction. The task of source integration in the Unified Medical Language System (UMLS) requires considerable expert effort despite the presence of various computational tools. This problem warrants the search for a new method for approximate string matching and its UMLS-based evaluation.
  
  
  RESULTS
  This paper introduces the Longest Approximately Common Prefix (LACP) method as an algorithm for approximate string matching that runs in linear time. We compare the LACP method for performance, precision and speed to nine other well-known string matching algorithms. As test data, we use two multiple-source samples from the Unified Medical Language System (UMLS) and two SNOMED Clinical Terms-based samples. In addition, we present a spell checker based on the LACP method.
  
  
  CONCLUSIONS
  The Longest Approximately Common Prefix method completes its string similarity evaluations in less time than all nine string similarity methods used for comparison. The Longest Approximately Common Prefix outperforms these nine approximate string matching methods in its Maximum F1 measure when evaluated on three out of the four datasets, and in its average precision on two of the four datasets.
authors.forename: Alex
authors.initials: A
authors.lastname: Rudniy
authors.affiliation: Department of Library and Information Science, Yonsei University, 50 Yonsei-ro, Seoul 120-749, Korea. min.song@yonsei.ac.kr.
authors.forename: Min
authors.initials: M
authors.lastname: Song
authors.forename: James
authors.initials: J
authors.lastname: Geller
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D006801:Humans; D007802:Language; D009323:Natural Language Processing; D009626:Terminology as Topic; D017432:Unified Medical Language System