arrow
Return

Towards a framework for developing semantic relatedness reference standards

delete2011-04-01
delete55
delete
OA
AI
S
Serguei Pakhomov *
T
Ted Pedersen
B
Bridget T. McInnes
G
Genevieve B. Melton
A
Alexander Ruggieri
C
Christopher G. Chute
DOI:10.1016/j.jbi.2010.10.004delete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
Our objective is to develop a framework for creating reference standards for functional testing of computerized measures of semantic relatedness. Currently, research on computerized approaches to semantic relatedness between biomedical concepts relies on reference standards created for specific purposes using a variety of methods for their analysis. In most cases, these reference standards are not publicly available and the published information provided in manuscripts that evaluate computerized semantic relatedness measurement approaches is not sufficient to reproduce the results. Our proposed framework is based on the experiences of medical informatics and computational linguistics communities and addresses practical and theoretical issues with creating reference standards for semantic relatedness. We demonstrate the use of the framework on a pilot set of 101 medical term pairs rated for semantic relatedness by 13 medical coding experts. While the reliability of this particular reference standard is in the moderate range; we show that using clustering and factor analyses offers a data-driven approach to finding systematic differences among raters and identifying groups of potential outliers. We test two ontology-based measures of relatedness and provide both the reference standard containing individual ratings and the R program used to analyze the ratings as open-source. Currently, these resources are intended to be used to reproduce and compare results of studies involving computerized measures of semantic relatedness. Our framework may be extended to the development of reference standards in other research areas in medical informatics including automatic classification, information retrieval from medical records and vocabulary/ontology development. (c) 2010 Elsevier Inc. All rights reserved.
Keywords:
Semantic relatedness
Reference standards
Reliability
Inter-annotator agreement
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Journal of Biomedical Informatics cover
Journal of Biomedical Informatics
IF:
4.5
Papers:
3.5K
Citations:
1.9W

Organization

U
University of Minnesota Twin Cities
Scholars:
3.7W
Papers: 3.1W
Citations: 58
U
University of Minnesota Duluth
Scholars:
1.3K
Papers: 1.0K
Citations: 1
Cited Papers

Cited Papers

errShare
errSave
errShare
errSave
Measures of semantic similarity and relatedness in the biomedical domain
err2007-06-01
err374
errOAAI
errPedersen, Ted; Pakhomov, Serguei V. S.; Patwardhan, Siddharth; Chute, Christopher G.
errShare
errSave
Inter-patient distance metrics using SNOMED CT defining relationships
err2006-12-01
err53
errOAAI
errMelton, Genevieve B.; Parsons, Simon; Morrison, Frances P.; Rothschild, Adam S.; Markatou, Marianthi; Hripcsak, George
errShare
errSave
Appraisal of the MedDRA conceptual structure for describing and grouping adverse drug reactions
err2005-01-01
err76
PREAI
errBousquet, C; Lagier, G; Louët, ALL; Le Beller, C; Venot, A; Jaulent, MC
errShare
errSave
THE DEVELOPMENT OF METHODS TO DETERMINE WEED SEED CONTAMINATION IN MUNICIPAL COMPOSTS
err1998-07-01
err0
PREAI
errAndrea C. Grundy; J. M. Green; B. Bond; S. Burston; M. Lennartsson
errShare
errSave
researcher View more