Overcoming an obstacle in expanding a UMLS semantic type extent

Yan Chen, Huanying Gu, Yehoshua Perl, James Geller

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


This paper strives to overcome a major problem encountered by a previous expansion methodology for discovering concepts highly likely to be missing a specific semantic type assignment in the UMLS. This methodology is the basis for an algorithm that presents the discovered concepts to a human auditor for review and possible correction. We analyzed the problem of the previous expansion methodology and discovered that it was due to an obstacle constituted by one or more concepts assigned the UMLS Semantic Network semantic type Classification. A new methodology was designed that bypasses such an obstacle without a combinatorial explosion in the number of concepts presented to the human auditor for review. The new expansion methodology with obstacle avoidance was tested with the semantic type Experimental Model of Disease and found over 500 concepts missed by the previous methodology that are in need of this semantic type assignment. Furthermore, other semantic types suffering from the same major problem were discovered, indicating that the methodology is of more general applicability. The algorithmic discovery of concepts that are likely missing a semantic type assignment is possible even in the face of obstacles, without an explosion in the number of processed concepts.

Original languageEnglish (US)
Pages (from-to)61-70
Number of pages10
JournalJournal of Biomedical Informatics
Issue number1
StatePublished - Feb 2012

All Science Journal Classification (ASJC) codes

  • Health Informatics
  • Computer Science Applications


  • Auditing
  • Group auditing
  • Neighborhood auditing
  • Refined semantic type
  • Semantic type assignment
  • UMLS


Dive into the research topics of 'Overcoming an obstacle in expanding a UMLS semantic type extent'. Together they form a unique fingerprint.

Cite this