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Hierarchical, perceptron-like learning for ontology-based information extraction

Authors:

Kalina BontchevaAuthors Info & Claims

WWW '07: Proceedings of the 16th international conference on World Wide Web

Pages 777 - 786

https://doi.org/10.1145/1242572.1242677

Published: 08 May 2007 Publication History

Abstract

Recent work on ontology-based Information Extraction (IE) has tried to make use of knowledge from the target ontology in order to improve semantic annotation results. However, very few approaches exploit the ontology structure itself, and those that do so, have some limitations. This paper introduces a hierarchical learning approach for IE, which uses the target ontology as an essential part of the extraction process, by taking into account the relations between concepts. The approach is evaluated on the largest available semantically annotated corpus. The results demonstrate clearly the benefits of using knowledge from the ontology as input to the information extraction process. We also demonstrate the advantages of our approach over other state-of-the-art learning systems on a commonly used benchmark dataset.

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Cited By

Bayrakdar SYucedag ISimsek MDogru I(2020)Semantic analysis on social networks: A surveyInternational Journal of Communication Systems10.1002/dac.442433:11Online publication date: 16-Apr-2020
https://doi.org/10.1002/dac.4424
Baracho FAbrantes Baracho RBonatti RF. Silva C(2018)Mitigating Risks by Weighting Intangibles when Investing in Renewables2018 7th International Conference on Renewable Energy Research and Applications (ICRERA)10.1109/ICRERA.2018.8566916(582-593)Online publication date: Oct-2018
https://doi.org/10.1109/ICRERA.2018.8566916
Pereira JGoncalves MSantos M(2017)Semantic annotation in historical documents2017 12th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI.2017.7975968(1-7)Online publication date: Jun-2017
https://doi.org/10.23919/CISTI.2017.7975968
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Index Terms

Hierarchical, perceptron-like learning for ontology-based information extraction
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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Published In

cover image ACM Conferences

WWW '07: Proceedings of the 16th international conference on World Wide Web

May 2007

1382 pages

ISBN:9781595936547

DOI:10.1145/1242572

General Chairs:
Carey Williamson
University of Calgary, Canada
,
Mary Ellen Zurko
IBM, USA
,
Program Chairs:
Peter Patel-Schneider
Bell Labs Research, USA
,
Prashant Shenoy
University of Massachusetts at Amherst, USA

Copyright © 2007 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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Published: 08 May 2007

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WWW'07

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ACM

WWW'07: 16th International World Wide Web Conference

May 8 - 12, 2007

Alberta, Banff, Canada

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Bayrakdar SYucedag ISimsek MDogru I(2020)Semantic analysis on social networks: A surveyInternational Journal of Communication Systems10.1002/dac.442433:11Online publication date: 16-Apr-2020
https://doi.org/10.1002/dac.4424
Baracho FAbrantes Baracho RBonatti RF. Silva C(2018)Mitigating Risks by Weighting Intangibles when Investing in Renewables2018 7th International Conference on Renewable Energy Research and Applications (ICRERA)10.1109/ICRERA.2018.8566916(582-593)Online publication date: Oct-2018
https://doi.org/10.1109/ICRERA.2018.8566916
Pereira JGoncalves MSantos M(2017)Semantic annotation in historical documents2017 12th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI.2017.7975968(1-7)Online publication date: Jun-2017
https://doi.org/10.23919/CISTI.2017.7975968
Pereira RCosta CAparicio J(2017)Gamification to support programming learning2017 12th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI.2017.7975788(1-6)Online publication date: Jun-2017
https://doi.org/10.23919/CISTI.2017.7975788
Lv XEl-Gohary N(2016)Semantic Annotation for Supporting Context-Aware Information Retrieval in the Transportation Project Environmental Review DomainJournal of Computing in Civil Engineering10.1061/(ASCE)CP.1943-5487.000056530:6Online publication date: Nov-2016
https://doi.org/10.1061/(ASCE)CP.1943-5487.0000565
Liu ZShan JGlassey Balet NFang G(2016)Semantic social media analysis of Chinese tourists in SwitzerlandInformation Technology & Tourism10.1007/s40558-016-0066-z17:2(183-202)Online publication date: 25-Oct-2016
https://doi.org/10.1007/s40558-016-0066-z
Sharma DJain S(2015)Context-based weighting for vector space model to evaluate the relation between concept and context in information storage and retrieval system2015 International Conference on Computer, Communication and Control (IC4)10.1109/IC4.2015.7375682(1-5)Online publication date: Sep-2015
https://doi.org/10.1109/IC4.2015.7375682
Kordjamshidi PMoens M(2015)Global machine learning for spatial ontology populationWeb Semantics: Science, Services and Agents on the World Wide Web10.1016/j.websem.2014.06.00130:C(3-21)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1016/j.websem.2014.06.001
Nakanishi T(2015)A Discovery Method of Anteroposterior Correlation for Big Data EraSoftware Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing10.1007/978-3-319-10389-1_12(161-177)Online publication date: 2015
https://doi.org/10.1007/978-3-319-10389-1_12
Nakanishi T(2014)Semantic Context-Dependent Weighting for Vector Space ModelProceedings of the 2014 IEEE International Conference on Semantic Computing10.1109/ICSC.2014.49(262-266)Online publication date: 16-Jun-2014
https://dl.acm.org/doi/10.1109/ICSC.2014.49
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