research-article

Semantic relatedness study using second order co-occurrence vectors computed from biomedical corpora, UMLS and WordNet

Authors:

Bridget T. McInnes,

Genevieve Melton-Meaux,

Serguei PakhomovAuthors Info & Claims

IHI '12: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

Pages 363 - 372

https://doi.org/10.1145/2110363.2110405

Published: 28 January 2012 Publication History

Abstract

Automated measures of semantic relatedness are important for effectively processing medical data for a variety of tasks such as information retrieval and natural language processing. In this paper, we present a context vector approach that can compute the semantic relatedness between any pair of concepts in the Unified Medical Language System (UMLS). Our approach has been developed on a corpus of inpatient clinical reports. We use 430 pairs of clinical concepts manually rated for semantic relatedness as the reference standard. The experiments demonstrate that incorporating a combination of the UMLS and WordNet definitions can improve the semantic relatedness. The paper also shows that second order co-occurrence vector measure is a more effective approach than path-based methods for semantic relatedness.

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Cited By

Zhao YYin JZhang JWu L(2023)Identifying the driving factors of word co-occurrence: a perspective of semantic relationsScientometrics10.1007/s11192-023-04851-x128:12(6471-6494)Online publication date: 30-Oct-2023
https://doi.org/10.1007/s11192-023-04851-x
El Ghosh MDelestre NKotowicz JZanni-Merk CAbdulrab H(2022)Rel Topic : A graph-based semantic relatedness measure in topic ontologies and its applicability for topic labeling of old press articlesSemantic Web10.3233/SW-22291914:2(293-321)Online publication date: 15-Dec-2022
https://doi.org/10.3233/SW-222919
Chang E(2022)A vector-based semantic relatedness measure using multiple relations within SNOMED CT and UMLSJournal of Biomedical Informatics10.1016/j.jbi.2022.104118131:COnline publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.jbi.2022.104118
Show More Cited By

Index Terms

Semantic relatedness study using second order co-occurrence vectors computed from biomedical corpora, UMLS and WordNet
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Published In

cover image ACM Conferences

IHI '12: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

January 2012

914 pages

ISBN:9781450307819

DOI:10.1145/2110363

General Chairs:
Gang Luo
IBM Research, USA
,
Jiming Liu
Hong Kong Baptist University
,
Program Chair:
Christopher C. Yang
Drexel University

Copyright © 2012 ACM.

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SIGHIT: ACM Special Interest Group on Health Informatics

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Association for Computing Machinery

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Published: 28 January 2012

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IHI '12: ACM International Health Informatics Symposium

January 28 - 30, 2012

Florida, Miami, USA

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Cited By

Zhao YYin JZhang JWu L(2023)Identifying the driving factors of word co-occurrence: a perspective of semantic relationsScientometrics10.1007/s11192-023-04851-x128:12(6471-6494)Online publication date: 30-Oct-2023
https://doi.org/10.1007/s11192-023-04851-x
El Ghosh MDelestre NKotowicz JZanni-Merk CAbdulrab H(2022)Rel Topic : A graph-based semantic relatedness measure in topic ontologies and its applicability for topic labeling of old press articlesSemantic Web10.3233/SW-22291914:2(293-321)Online publication date: 15-Dec-2022
https://doi.org/10.3233/SW-222919
Chang E(2022)A vector-based semantic relatedness measure using multiple relations within SNOMED CT and UMLSJournal of Biomedical Informatics10.1016/j.jbi.2022.104118131:COnline publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.jbi.2022.104118
Jing X(2021)The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content AnalysisJMIR Medical Informatics10.2196/206759:8(e20675)Online publication date: 27-Aug-2021
https://doi.org/10.2196/20675
Jebali ASassi SJemai AChbeir R(2021)Secure data outsourcing in presence of the inference problem: A graph-based approachJournal of Parallel and Distributed Computing10.1016/j.jpdc.2021.09.006Online publication date: Oct-2021
https://doi.org/10.1016/j.jpdc.2021.09.006
Heo GXie Q(2019)A Hybrid Semantic Relatedness Algorithm by Entity Co-Occurrence and Specialized Word Embeddings2019 IEEE International Conference on Healthcare Informatics (ICHI)10.1109/ICHI.2019.8904663(1-2)Online publication date: Jun-2019
https://doi.org/10.1109/ICHI.2019.8904663
Pesaranghader AMatwin SSokolova MPesaranghader A(2019)deepBioWSD: effective deep neural word sense disambiguation of biomedical text dataJournal of the American Medical Informatics Association10.1093/jamia/ocy189Online publication date: 26-Feb-2019
https://doi.org/10.1093/jamia/ocy189
Park JKim KHwang WLee D(2019)Concept Embedding to Measure Semantic Relatedness for Biomedical Information OntologiesJournal of Biomedical Informatics10.1016/j.jbi.2019.103182(103182)Online publication date: Apr-2019
https://doi.org/10.1016/j.jbi.2019.103182
Guo XLi RYu QHaake A(2017)Modeling physicians' utterances to explore diagnostic decision-makingProceedings of the 26th International Joint Conference on Artificial Intelligence10.5555/3172077.3172406(3700-3706)Online publication date: 19-Aug-2017
https://dl.acm.org/doi/10.5555/3172077.3172406
Zhu YYan EWang F(2017)Semantic relatedness and similarity of biomedical terms: examining the effects of recency, size, and section of biomedical publications on the performance of word2vecBMC Medical Informatics and Decision Making10.1186/s12911-017-0498-117:1Online publication date: 3-Jul-2017
https://doi.org/10.1186/s12911-017-0498-1
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