research-article

Graph-based concept weighting for medical information retrieval

Authors:

Laurianne Sitbon,

Michael LawleyAuthors Info & Claims

ADCS '12: Proceedings of the Seventeenth Australasian Document Computing Symposium

Pages 80 - 87

https://doi.org/10.1145/2407085.2407096

Published: 05 December 2012 Publication History

Abstract

This paper presents a graph-based method to weight medical concepts in documents for the purposes of information retrieval. Medical concepts are extracted from free-text documents using a state-of-the-art technique that maps n-grams to concepts from the SNOMED CT medical ontology. In our graph-based concept representation, concepts are vertices in a graph built from a document, edges represent associations between concepts. This representation naturally captures dependencies between concepts, an important requirement for interpreting medical text, and a feature lacking in bag-of-words representations.

We apply existing graph-based term weighting methods to weight medical concepts. Using concepts rather than terms addresses vocabulary mismatch as well as encapsulates terms belonging to a single medical entity into a single concept. In addition, we further extend previous graph-based approaches by injecting domain knowledge that estimates the importance of a concept within the global medical domain.

Retrieval experiments on the TREC Medical Records collection show our method outperforms both term and concept baselines. More generally, this work provides a means of integrating background knowledge contained in medical ontologies into data-driven information retrieval approaches.

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Cited By

Yang SZheng XXiao YYang YZhao D(2021)Combining Query Reformulation and Re-ranking to Improve Query Expansion in Chinese EMR Retrieval2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM52615.2021.9669713(2912-2919)Online publication date: 9-Dec-2021
https://doi.org/10.1109/BIBM52615.2021.9669713
Scells HZuccon GKoopman B(2019)Automatic Boolean Query Refinement for Systematic Review Literature SearchThe World Wide Web Conference10.1145/3308558.3313544(1646-1656)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313544
Torjmen-Khemakhem MGasmi K(2019)Document/Query Expansion based on Selecting Significant Concepts for Context Based Retrieval of Medical ImagesJournal of Biomedical Informatics10.1016/j.jbi.2019.103210(103210)Online publication date: May-2019
https://doi.org/10.1016/j.jbi.2019.103210
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Index Terms

Graph-based concept weighting for medical information retrieval
1. Information systems
  1. Information retrieval

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Abstract
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cover image ACM Other conferences

ADCS '12: Proceedings of the Seventeenth Australasian Document Computing Symposium

December 2012

142 pages

ISBN:9781450314114

DOI:10.1145/2407085

Conference Chair:
Andrew Trotman
University of Otago
,
Program Chairs:
Sally Jo Cunningham
University of Waikato
,
Laurianne Sitbon
Queensland University of Technology

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Dept. of Information Science, Univ.of Otago: Department of Information Science, University of Otago, Dunedin, New Zealand

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 December 2012

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ADCS '12

Sponsor:

Dept. of Information Science, Univ.of Otago

ADCS '12: The Seventeenth Australasian Document Computing Symposium

December 5 - 6, 2012

Dunedin, New Zealand

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Overall Acceptance Rate 30 of 57 submissions, 53%

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Cited By

Yang SZheng XXiao YYang YZhao D(2021)Combining Query Reformulation and Re-ranking to Improve Query Expansion in Chinese EMR Retrieval2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM52615.2021.9669713(2912-2919)Online publication date: 9-Dec-2021
https://doi.org/10.1109/BIBM52615.2021.9669713
Scells HZuccon GKoopman B(2019)Automatic Boolean Query Refinement for Systematic Review Literature SearchThe World Wide Web Conference10.1145/3308558.3313544(1646-1656)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313544
Torjmen-Khemakhem MGasmi K(2019)Document/Query Expansion based on Selecting Significant Concepts for Context Based Retrieval of Medical ImagesJournal of Biomedical Informatics10.1016/j.jbi.2019.103210(103210)Online publication date: May-2019
https://doi.org/10.1016/j.jbi.2019.103210
Zuccon GKoopman BCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)SIGIR 2018 Tutorial on Health Search (HS2018)The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210188(1391-1394)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210188
Hojas-Mazo WSimón-Cuevas Ade la Iglesia Campos MRomero FOlivas J(2018)A Concept-Based Text Analysis Approach Using Knowledge GraphInformation Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations10.1007/978-3-319-91476-3_57(696-708)Online publication date: 18-May-2018
https://doi.org/10.1007/978-3-319-91476-3_57
VENINGSTON KSHANMUGALAKSHMI RNIRMALA V(2015)Semantic association ranking schemes for information retrieval applications using term association graph representationSadhana10.1007/s12046-015-0413-340:6(1793-1819)Online publication date: 7-Nov-2015
https://doi.org/10.1007/s12046-015-0413-3
Cohen TWiddows DRindflesch T(2015)Expansion-by-Analogy: A Vector Symbolic Approach to Semantic SearchQuantum Interaction10.1007/978-3-319-15931-7_5(54-66)Online publication date: 20-Feb-2015
https://doi.org/10.1007/978-3-319-15931-7_5
Kharazmi SKarimi SScholer FClark A(2014)A Study of Querying Behaviour of Expert and Non-expert Users of Biomedical Search SystemsProceedings of the 19th Australasian Document Computing Symposium10.1145/2682862.2682871(10-17)Online publication date: 26-Nov-2014
https://dl.acm.org/doi/10.1145/2682862.2682871
Wittek PKoopman BZuccon GDarányi S(2014)Combining Word Semantics within Complex Hilbert Space for Information RetrievalQuantum Interaction10.1007/978-3-662-45912-6_14(160-171)Online publication date: 18-Apr-2014
https://doi.org/10.1007/978-3-662-45912-6_14
Wittek PKoopman BZuccon GDarányi S(2013)Combining Word Semantics within Complex Hilbert Space for Information RetrievalSelected Papers of the 7th International Conference on Quantum Interaction - Volume 836910.1007/978-3-642-54943-4_14(160-171)Online publication date: 25-Jul-2013
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