research-article

Ontology-Based Mapping for Automated Document Management: A Concept-Based Technique for Word Mismatch and Ambiguity Problems in Document Clustering

Authors:

Paul Jen-Hwa Hu,

Ching-Yi TuAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 6, Issue 1

Article No.: 4, Pages 1 - 22

https://doi.org/10.1145/2688488

Published: 04 March 2015 Publication History

Abstract

Document clustering is crucial to automated document management, especially for the fast-growing volume of textual documents available digitally. Traditional lexicon-based approaches depend on document content analysis and measure overlap of the feature vectors representing different documents, which cannot effectively address word mismatch or ambiguity problems. Alternative query expansion and local context discovery approaches are developed but suffer from limited efficiency and effectiveness, because the large number of expanded terms create noise and increase the dimensionality and complexity of the overall feature space. Several techniques extend lexicon-based analysis by incorporating latent semantic indexing but produce less comprehensible clustering results and questionable performance. We instead propose a concept-based document representation and clustering (CDRC) technique and empirically examine its effectiveness using 433 articles concerning information systems and technology, randomly selected from a popular digital library. Our evaluation includes two widely used benchmark techniques and shows that CDRC outperforms them. Overall, our results reveal that clustering documents at an ontology-based, concept-based level is more effective than techniques using lexicon-based document features and can generate more comprehensible clustering results.

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Index Terms

Ontology-Based Mapping for Automated Document Management: A Concept-Based Technique for Word Mismatch and Ambiguity Problems in Document Clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 6, Issue 1

April 2015

111 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/2742819

Editor:
Alexander Tuzhilin
New York University, USA

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Copyright © 2015 ACM.

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Publication History

Published: 04 March 2015

Accepted: 01 November 2014

Revised: 01 November 2014

Received: 01 March 2014

Published in TMIS Volume 6, Issue 1

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Cited By

Sutriawan SRustad SShidik GPujiono PMuljono M(2024)Review of ambiguity problem in text summarization using hybrid ACA and SLRIntelligent Systems with Applications10.1016/j.iswa.2024.20036022(200360)Online publication date: Jun-2024
https://doi.org/10.1016/j.iswa.2024.200360
Jasila ESaleena NAbdul Nazeer K(2023)An Efficient Document Clustering Approach for Devising Semantic ClustersCybernetics and Systems10.1080/01969722.2023.2175135(1-18)Online publication date: 11-Feb-2023
https://doi.org/10.1080/01969722.2023.2175135
Miksa TOblasser SRauber A(2021)Automating Research Data Management Using Machine-Actionable Data Management PlansACM Transactions on Management Information Systems10.1145/349039613:2(1-22)Online publication date: 11-Dec-2021
https://dl.acm.org/doi/10.1145/3490396
Akanfe OValecha RRao H(2020)Design of an Inclusive Financial Privacy Index (INF-PIE): A Financial Privacy and Digital Financial Inclusion PerspectiveACM Transactions on Management Information Systems10.1145/340394912:1(1-21)Online publication date: 22-Dec-2020
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Herzig JFeigenblat GShmueli-Scheuer MKonopnicki DRafaeli AVassileva JBlustein JAroyo LD'Mello S(2016)Predicting Customer Satisfaction in Customer Support Conversations in Social Media Using Affective FeaturesProceedings of the 2016 Conference on User Modeling Adaptation and Personalization10.1145/2930238.2930285(115-119)Online publication date: 13-Jul-2016
https://dl.acm.org/doi/10.1145/2930238.2930285

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