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Inference networks for document retrieval

Authors:

W. B. CroftAuthors Info & Claims

SIGIR '90: Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 1 - 24

https://doi.org/10.1145/96749.98006

Published: 01 December 1989 Publication History

Abstract

The use of inference networks to support document retrieval is introduced. A network-based retrieval model is described and compared to conventional probabilistic and Boolean models.

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Cited By

Chebil WWedyan MAlazab MAlturki RElshaweesh O(2023)Improving Semantic Information Retrieval Using Multinomial Naive Bayes Classifier and Bayesian NetworksInformation10.3390/info1405027214:5(272)Online publication date: 3-May-2023
https://doi.org/10.3390/info14050272
Charbel NSallaberry CLaborie SChbeir R(2023)FEED2SEARCH: a framework for hybrid-molecule based semantic searchInternational Journal of General Systems10.1080/03081079.2023.219517352:3(343-383)Online publication date: 24-Apr-2023
https://doi.org/10.1080/03081079.2023.2195173
Zhang LXu JGong YYu LZhang JShen J(2022)Unsupervised Image and Text Fusion for Travel Information EnhancementIEEE Transactions on Multimedia10.1109/TMM.2021.306440824(1415-1425)Online publication date: 2022
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Inference networks for document retrieval

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Inference Networks for Document Retrieval
SIGIR Test-of-Time Awardees 1978-2001

The use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilistic and Boolean models.
Inference networks for document retrieval
Inference Networks for Document Retrieval

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cover image ACM Conferences

SIGIR '90: Proceedings of the 13th annual international ACM SIGIR conference on Research and development in information retrieval

December 1989

509 pages

ISBN:0897914082

DOI:10.1145/96749

Editor:
Jean-Luc Vidick
Univ. Libre´ de Bruxelles, Bruxelles, Belgium

Copyright © 1989 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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SIGIR: ACM Special Interest Group on Information Retrieval
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 1989

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SIGIR'90: 13th ACM SIGIR International Conference on Research and Development in Information Retrieval

September 5 - 7, 1990

Brussels, Belgium

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Cited By

Chebil WWedyan MAlazab MAlturki RElshaweesh O(2023)Improving Semantic Information Retrieval Using Multinomial Naive Bayes Classifier and Bayesian NetworksInformation10.3390/info1405027214:5(272)Online publication date: 3-May-2023
https://doi.org/10.3390/info14050272
Charbel NSallaberry CLaborie SChbeir R(2023)FEED2SEARCH: a framework for hybrid-molecule based semantic searchInternational Journal of General Systems10.1080/03081079.2023.219517352:3(343-383)Online publication date: 24-Apr-2023
https://doi.org/10.1080/03081079.2023.2195173
Zhang LXu JGong YYu LZhang JShen J(2022)Unsupervised Image and Text Fusion for Travel Information EnhancementIEEE Transactions on Multimedia10.1109/TMM.2021.306440824(1415-1425)Online publication date: 2022
https://doi.org/10.1109/TMM.2021.3064408
Dorn J(2022)Web Information Retrieval and SearchHandbook of e-Tourism10.1007/978-3-030-48652-5_16(253-272)Online publication date: 2-Sep-2022
https://doi.org/10.1007/978-3-030-48652-5_16
Graef RKlier MKluge KZolitschka J(2020)Human-machine collaboration in online customer service – a long-term feedback-based approachElectronic Markets10.1007/s12525-020-00420-931:2(319-341)Online publication date: 6-May-2020
https://doi.org/10.1007/s12525-020-00420-9
Frittelli VDiván M(2020)An Architecture for e-Health Recommender Systems Based on Similarity of Patients’ SymptomsBlockchain Technology for Smart Cities10.1007/978-981-15-2205-5_8(155-180)Online publication date: 8-Feb-2020
https://doi.org/10.1007/978-981-15-2205-5_8
Du KSwamy MDu KSwamy M(2019)Data MiningNeural Networks and Statistical Learning10.1007/978-1-4471-7452-3_30(871-903)Online publication date: 13-Sep-2019
https://doi.org/10.1007/978-1-4471-7452-3_30
Chatterjee SSarkar K(2018)Combining IR Models for Bengali Information RetrievalInternational Journal of Information Retrieval Research10.4018/IJIRR.20180701058:3(68-83)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.4018/IJIRR.2018070105
Dong HLu PLiu CJi YGong S(2018)Learning Multiple Kernel Metrics for Iterative Person Re-IdentificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/323492914:3(1-24)Online publication date: 9-Aug-2018
https://dl.acm.org/doi/10.1145/3234929
Kiess JKopf SGuthier BEffelsberg W(2018)A Survey on Content-Aware Image and Video RetargetingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/323159814:3(1-28)Online publication date: 24-Jul-2018
https://dl.acm.org/doi/10.1145/3231598
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