A Study on the Retrieval Effectiveness of KoreaMed using MeSH Search Filter and Word-Proximity Search

검색용 MeSH 필터와 단어인접탐색 기법을 활용한 KoreaMed 검색 효율성 향상 연구

  • Jeong, So-Na (Medical Library, Catholic University of Korea) ;
  • Jeong, Ji-Na (Department of Health management, Jeonju University)
  • 정소나 (가톨릭대학교 성의교정 도서관) ;
  • 정지나 (전주대학교 보건관리학과)
  • Received : 2017.03.31
  • Accepted : 2017.05.12
  • Published : 2017.05.31


This study examined the method for adding related to "stomach neoplasms" as filters to the Medical Subject Headings (MeSH) for search as well as a method for improving the search efficiency through a word-proximity search by measuring the distance of co-occurring terms. A total of 8,625 articles published between 2007 and 2016 with the major topic terms "stomach neoplasms" were downloaded from PubMed article titles. The vocabulary to be added to the MeSH for search were analyzed. The search efficiency was verified by 277 articles that had "Stomach Neoplasms" indexed as MEDLINE MeSH in KoreaMed. As a result, 973 terms were selected as the candidate vocabulary. "Gastric Cancer" (2,780 appearances) was the most frequent term and 7,376 compound words (88.51%) combined the histological terms of "stomach" and "neoplasm", such as "gastric adenocarcinoma" and "gastric MALT lymphoma". A total of 5,234 compounds words (70.95%), in which the co-occurring distance was two words, were found. The matching rate through the MEDLINE MeSH and KoreaMed MeSH Indexer was 209 articles (75.5%). The search efficiency improved to 263 articles (94.9%) when the search filters were added, and to 268 articles (96.7%) when the 13 word-proximity search technique of the co-occurring terms was applied. This study showed that the use of a thesaurus as a means of improving the search efficiency in a natural language search could maintain the advantages of controlled vocabulary. The search accuracy can be improved using the word-proximity search instead of a Boolean search.


Medical Subject Headings;Retrieval Efficiency;Stomach Neoplasms;Co-word Analysis;Word-proximity Search;KoreaMed


  1. S. L. De Groote, M. Schultz, D. D. Blecic, "Information-seeking behavior and the use of online resources: a snapshot of current health sciences faculty", Journal of the Medical Library Association, vol. 102, no. 3, p. 169, 2014. DOI:
  2. US National Library Medicine. Fact Sheet Bibliographic Services Division,(BSD) 2017. [cited 2017 Mar 2], Available From: s/bsd.html.(accessed Mar., 31, 2017)
  3. S. N. Jeong, C. S. Lee, "MeSH Semi Indexing of the Korean Biomedical Literature, using NLM Medical Text Indexer", in, Korea Society for Information Management, pp. 21-28, 2010.
  4. Cochrane Library. How CENTRAL is created [cited 2017 Mar 31], Available From: ccessed Mar., 31, 2017)
  5. Cochrane Library. Cochrane Crowd [cited 2017 Mar 31], Available From: (accessed Mar., 31, 2017)
  6. D. L. Sackett, W. M. Rosenberg, J. A. Gray, R. B. Haynes, W. S. Richardson, "Evidence based medicine: what it is and what it isn't", BMJ, vol. 312, no. 7023, pp. 71-72, 1996. DOI:
  7. C. S. Lee, "Medical Database Search", Journal of the Korean Medical Association, vol. 53, no. 8, pp. 668-686, 2010. DOI:
  8. M. Macedo-Rouet, J. F. Rouet, C. Ros, N. Vibert, "How do scientists select articles in the PubMed database? An empirical study of criteria and strategies", Revue Europeenne de Psychologie Appliquee/European Review of Applied Psychology, vol. 62, no. 2, pp. 63-72, 2012. DOI:
  9. N. Baumann, "How to use the medical subject headings (MeSH)", International Journal of Clinical Practice, vol. 70, no. 2, pp. 171-174, 2016. DOI:
  10. Korean Statistical Information System National Statistical Office. Cancer occurrence and death status. 2017 [cited 2017 Mar 2], Available From: Mar. 31, 2017)
  11. US National Library of Medicine. Medical Subject Headings 2017. Available From: (accessed Mar., 31, 2017)
  12. A. Fritz, C. Percy, A. Jack, K. Shanmugaratnam, L. Sobin, D. M. Parkin, S. Whelan, International classification of diseases for oncology, World Health Organization, 2000.
  13. US National Library Medicine. Search Strategy Used to Create the Cancer Subset on PubMed. 2017 [cited 2017 Mar 2], Available From: Mar., 31, 2017)
  14. C. C. Compton, D. R. Byrd, J. Garcia-Aguilar, S. H. Kurtzman, A. Olawaiye, M. K. Washington, "AJCC cancer staging atlas", pp. 143-153, Springer, New York, 2012. DOI:
  15. US National Library of Medicine. MeSH on Demand. Available From: Mar., 31, 2017)
  16. D. R. Swanson, N. R. Smalheiser, V. I. Torvik, "Ranking indirect connections in literature‐based discovery: The role of medical subject headings," Journal of the American Society for Information Science and Technology, vol. 57, no. 11, pp. 1427-1439, 2006. DOI:
  17. S. Y. Bong, K. B. Hwang, "A Method for Author Keyphrase Recommendation for Bioinformatics Papers Using Assigned MeSH Terms", The HCI Society of Korea, pp. 236-238, 2011.
  18. J. G. Mork, A. J. Jimeno-Yepes, A. R. Aronson, "The NLM Medical Text Indexer System for Indexing Biomedical Literature", in BioASQ@ CLEF, 2013.
  19. A. Jimeno-Yepes, J. G. Mork, D. Demner-Fushman, A. R. Aronson, "A one-size-fits-all indexing method does not exist: automatic selection based on meta-learning", Journal of Computing Science and Engineering, vol. 6, no. 2, pp. 151-160, 2012. DOI:
  20. ICHUSI Web. 2017 [cited 2017 Mar 2], : Available From (accessed Mar. 31, 2017)
  21. US National Library Medicine. How can I become an indexer? 2017 [cited 2017 Mar 2], Available From: Mar., 31, 2017)
  22. G. S. Go, W. K. Jung, Y. G. Shin, S. S. Park, "A Study on development of patent information retrieval using textmining", Journal of the Korean Academia-Industrial cooperation Society, vol. 12, no. 8, pp. 3677-3688, 2011. DOI:
  23. US National Library of Medicine. Unified Medical Language System (UMLS). Available From: (accessed Mar., 31, 2017)