research-article

Exploiting Inference from Semantic Annotations for Information Retrieval: Reflections From Medical IR

Authors:

Peter BruzaAuthors Info & Claims

ESAIR '14: Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval

Pages 43 - 45

https://doi.org/10.1145/2663712.2666197

Published: 07 November 2014 Publication History

Abstract

The increasing amount of information that is annotated against standardised semantic resources offers opportunities to incorporate sophisticated levels of reasoning, or inference, into the retrieval process. In this position paper, we reflect on the need to incorporate semantic inference into retrieval (in particular for medical information retrieval) as well as previous attempts that have been made so far with mixed success. Medical information retrieval is a fertile ground for testing inference mechanisms to augment retrieval. The medical domain offers a plethora of carefully curated, structured, semantic resources, along with well established entity extraction and linking tools, and search topics that intuitively require a number of different inferential processes (e.g., conceptual similarity, conceptual implication, etc.). We argue that integrating semantic inference in information retrieval has the potential to uncover a large amount of information that otherwise would be inaccessible; but inference is also risky and, if not used cautiously, can harm retrieval.

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Cited By

Gudivada APhilips JTabrizi N(2021)Developing Concept Enriched Models for Big Data Processing Within the Medical DomainInternational Journal of Software Science and Computational Intelligence10.4018/IJSSCI.202007010512:3(55-71)Online publication date: 7-Dec-2021
https://doi.org/10.4018/IJSSCI.2020070105
Gudivada ATabrizi N(2019)Developing Concept Enriched Models for Processing Big Data Within the Medical Domain2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)10.1109/ICCICC46617.2019.9146074(222-229)Online publication date: Jul-2019
https://doi.org/10.1109/ICCICC46617.2019.9146074
de Souza WPereira ÁRoesler VValdeni de Lima JSaibel Santos CWillrich R(2017)A Methodology for Automatic Generation of Vocabulary ExercisesProceedings of the 23rd Brazillian Symposium on Multimedia and the Web10.1145/3126858.3131566(209-212)Online publication date: 17-Oct-2017
https://dl.acm.org/doi/10.1145/3126858.3131566
Show More Cited By

Index Terms

Exploiting Inference from Semantic Annotations for Information Retrieval: Reflections From Medical IR
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval models and ranking

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cover image ACM Conferences

ESAIR '14: Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval

November 2014

52 pages

ISBN:9781450313650

DOI:10.1145/2663712

General Chairs:
Omar Alonso
Microsoft, Mountain View CA, USA
,
Jaap Kamps
University of Amsterdam, The Netherlands
,
Jussi Karlgren
Gavagai Stockholm, Sweden

Copyright © 2014 ACM.

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Published: 07 November 2014

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CIKM '14: 2014 ACM Conference on Information and Knowledge Management

November 7, 2014

Shanghai, China

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ESAIR '14 Paper Acceptance Rate 11 of 15 submissions, 73%;

Overall Acceptance Rate 35 of 55 submissions, 64%

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Cited By

Gudivada APhilips JTabrizi N(2021)Developing Concept Enriched Models for Big Data Processing Within the Medical DomainInternational Journal of Software Science and Computational Intelligence10.4018/IJSSCI.202007010512:3(55-71)Online publication date: 7-Dec-2021
https://doi.org/10.4018/IJSSCI.2020070105
Gudivada ATabrizi N(2019)Developing Concept Enriched Models for Processing Big Data Within the Medical Domain2019 IEEE 18th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)10.1109/ICCICC46617.2019.9146074(222-229)Online publication date: Jul-2019
https://doi.org/10.1109/ICCICC46617.2019.9146074
de Souza WPereira ÁRoesler VValdeni de Lima JSaibel Santos CWillrich R(2017)A Methodology for Automatic Generation of Vocabulary ExercisesProceedings of the 23rd Brazillian Symposium on Multimedia and the Web10.1145/3126858.3131566(209-212)Online publication date: 17-Oct-2017
https://dl.acm.org/doi/10.1145/3126858.3131566
Wang HZhang QYuan J(2017)Semantically Enhanced Medical Information Retrieval System: A Tensor Factorization Based ApproachIEEE Access10.1109/ACCESS.2017.26981425(7584-7593)Online publication date: 2017
https://doi.org/10.1109/ACCESS.2017.2698142
Foley JO'Connor BAllan JMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Improving Entity Ranking for Keyword QueriesProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983909(2061-2064)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983909
Alonso OKamps JKarlgren J(2015)Report on the Seventh Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR'14)ACM SIGIR Forum10.1145/2795403.279541249:1(27-34)Online publication date: 23-Jun-2015
https://dl.acm.org/doi/10.1145/2795403.2795412
Alonso OKamps JKarlgren JLi JWang XGarofalakis MSoboroff ISuel TWang M(2014)Seventh Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR'14)Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management10.1145/2661829.2663539(2094-2095)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2661829.2663539

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