Article

Altering document term vectors for classification: ontologies as expectations of co-occurrence

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Mustafa UysalAuthors Info & Claims

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

Pages 1225 - 1226

https://doi.org/10.1145/1242572.1242778

Published: 08 May 2007 Publication History

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Abstract

In this paper we extend the state-of-the-art in utilizing background knowledge for supervised classification by exploiting the semantic relationships between terms explicated in Ontologies. Preliminary evaluations indicate that the new approach generally improves precision and recall, more so for hard to classify cases and reveals patterns indicating the usefulness of such background knowledge.

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Cited By

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Sell DTrierveiler HSchneider VHenriqson EMorales ATodesco JSelig P(2022)Framework for the Analysis of Resilient Performance Conditionings in Integrated Operations of the Oil and Gas IndustryResilience in a Digital Age10.1007/978-3-030-85954-1_6(71-92)Online publication date: 11-Mar-2022
https://doi.org/10.1007/978-3-030-85954-1_6
Costa RLima C(2018)Document Clustering Using an Ontology-Based Vector Space ModelInformation Retrieval and Management10.4018/978-1-5225-5191-1.ch085(1860-1883)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5191-1.ch085
Costa RFigueiras PJardim-Goncalves RRamos-Filho JLima C(2017)Semantic enrichment of product data supported by machine learning techniques2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)10.1109/ICE.2017.8280056(1472-1479)Online publication date: Jun-2017
https://doi.org/10.1109/ICE.2017.8280056
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Index Terms

Altering document term vectors for classification: ontologies as expectations of co-occurrence
1. Information systems
  1. Information retrieval
    1. Document representation
    2. Search engine architectures and scalability
      1. Search engine indexing

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Published In

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

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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Association for Computing Machinery

New York, NY, United States

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Published: 08 May 2007

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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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Sell DTrierveiler HSchneider VHenriqson EMorales ATodesco JSelig P(2022)Framework for the Analysis of Resilient Performance Conditionings in Integrated Operations of the Oil and Gas IndustryResilience in a Digital Age10.1007/978-3-030-85954-1_6(71-92)Online publication date: 11-Mar-2022
https://doi.org/10.1007/978-3-030-85954-1_6
Costa RLima C(2018)Document Clustering Using an Ontology-Based Vector Space ModelInformation Retrieval and Management10.4018/978-1-5225-5191-1.ch085(1860-1883)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5191-1.ch085
Costa RFigueiras PJardim-Goncalves RRamos-Filho JLima C(2017)Semantic enrichment of product data supported by machine learning techniques2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC)10.1109/ICE.2017.8280056(1472-1479)Online publication date: Jun-2017
https://doi.org/10.1109/ICE.2017.8280056
Davydov MLozynska O(2017)Mathematical Method of Translation into Ukrainian Sign Language Based on OntologiesAdvances in Intelligent Systems and Computing II10.1007/978-3-319-70581-1_7(89-100)Online publication date: 22-Nov-2017
https://doi.org/10.1007/978-3-319-70581-1_7
Sanchez-Pi NMartí LBicharra Garcia A(2016)Improving ontology-based text classificationJournal of Applied Logic10.1016/j.jal.2015.09.00817:C(48-58)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.jal.2015.09.008
Costa RLima C(2015)Document Clustering Using an Ontology-Based Vector Space ModelInternational Journal of Information Retrieval Research10.4018/IJIRR.20150701035:3(39-60)Online publication date: Jul-2015
https://doi.org/10.4018/IJIRR.2015070103
Costa RLima C(2015)Management of Knowledge Sources Supported by Domain OntologiesInternational Journal of Intelligent Systems in Accounting and Finance Management10.1002/isaf.136122:1(29-64)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1002/isaf.1361
Sanchez-Pi NMartí LGarcia A(2014)Text Classification Techniques in Oil Industry ApplicationsInternational Joint Conference SOCO’13-CISIS’13-ICEUTE’1310.1007/978-3-319-01854-6_22(211-220)Online publication date: 2014
https://doi.org/10.1007/978-3-319-01854-6_22
Cabrera JEscalante HMontes-y-Gómez M(2013)Distributional term representations for short-text categorizationProceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 210.1007/978-3-642-37256-8_28(335-346)Online publication date: 24-Mar-2013
https://dl.acm.org/doi/10.1007/978-3-642-37256-8_28
Shen DWu JCao BSun JYang QChen ZLi YCheung DSong IChu WHu XLin J(2009)Exploiting term relationship to boost text classificationProceedings of the 18th ACM conference on Information and knowledge management10.1145/1645953.1646192(1637-1640)Online publication date: 2-Nov-2009
https://dl.acm.org/doi/10.1145/1645953.1646192
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Independent document ranking for E-learning using semantic-based document term classification

Efficient Feature Selection and Domain Relevance Term Weighting Method for Document Classification

A multiclass/multilabel document categorization system: Combining multiple classifiers in a reduced dimension

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