Evaluation and application of a semantic network partition

James Geller, Yehoshua Perl, Michael Halper, Zong Chen, Huanying Gu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Semantic networks (SNs) are excellent knowledge representation structures. However, large semantic networks are hard to comprehend. To overcome this difficulty, several methods of partitioning have been developed that rely on different mixes of structural and semantic methods. However, little has appeared in the literature concerning the question whether a partition of a semantic network creates subnetworks that agree with human insight. We address this issue by presenting a comparison between the results of an algorithmic partitioning method and a partition created by a group of experts. Subsequently, we show how a network partition can be used to generate various partial views of a semantic network, which facilitate user orientation. Examples from the Unified Medical Language System (UMLS) SN are used to demonstrate partial views.

Original languageEnglish (US)
Pages (from-to)109-115
Number of pages7
JournalIEEE Transactions on Information Technology in Biomedicine
Volume6
Issue number2
DOIs
StatePublished - Jun 2002

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Evaluation
  • Orientation
  • Partial view
  • Partitioning
  • Semantic network (SN)
  • Semantic type
  • Subnetwork

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