research-article

Improving ontology-based text classification

Authors:

Nayat Sanchez-Pi,

Ana Cristina Bicharra GarciaAuthors Info & Claims

Journal of Applied Logic, Volume 17, Issue C

Pages 48 - 58

https://doi.org/10.1016/j.jal.2015.09.008

Published: 01 September 2016 Publication History

Abstract

Information retrieval has been widely studied due to the growing amounts of textual information available electronically. Nowadays organizations and industries are facing the challenge of organizing, analyzing and extracting knowledge from masses of unstructured information for decision making process. The development of automatic methods to produce usable structured information from unstructured text sources is extremely valuable to them. Opposed to the traditional text classification methods that need a set of well-classified trained corpus to perform efficient classification; the ontology-based classifier benefits from the domain knowledge and provides more accuracy. In a previous work we proposed and evaluated an ontology-based heuristic algorithm 28 for occupational health control process, particularly, for the case of automatic detection of accidents from unstructured texts. Our extended proposal is more domain dependent because it uses technical terms and contrast the relevance of these technical terms into the text, so the heuristic is more accurate. It divides the problem in subtasks such as: (i) text analysis, (ii) recognition and (iii) classification of failed occupational health control, resolving accidents as text analysis, recognition and classification of failed occupational health control, resolving accidents.

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Cited By

Sarkar SPramanik AMaiti J(2023)An integrated approach using rough set theory, ANFIS, and Z-number in occupational risk predictionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105515117:PAOnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.engappai.2022.105515
Guber JReinhartz-Berger ILitvak M(2023)Empirical Exploration of Open-Source Issues for Predicting Privacy ComplianceAdvances in Conceptual Modeling10.1007/978-3-031-47112-4_6(63-73)Online publication date: 6-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-47112-4_6
Jain SMalik S(2022)Knowledge-Infused Text Classification for the Biomedical DomainInternational Journal of Information System Modeling and Design10.4018/IJISMD.30663513:10(1-15)Online publication date: 16-Sep-2022
https://dl.acm.org/doi/10.4018/IJISMD.306635
Show More Cited By

Improving ontology-based text classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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cover image Journal of Applied Logic

Journal of Applied Logic Volume 17, Issue C

September 2016

58 pages

ISSN:1570-8683

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Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 September 2016

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Cited By

Sarkar SPramanik AMaiti J(2023)An integrated approach using rough set theory, ANFIS, and Z-number in occupational risk predictionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105515117:PAOnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.engappai.2022.105515
Guber JReinhartz-Berger ILitvak M(2023)Empirical Exploration of Open-Source Issues for Predicting Privacy ComplianceAdvances in Conceptual Modeling10.1007/978-3-031-47112-4_6(63-73)Online publication date: 6-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-47112-4_6
Jain SMalik S(2022)Knowledge-Infused Text Classification for the Biomedical DomainInternational Journal of Information System Modeling and Design10.4018/IJISMD.30663513:10(1-15)Online publication date: 16-Sep-2022
https://dl.acm.org/doi/10.4018/IJISMD.306635
Westhoven M(2022)Requirements for AI Support in Occupational Safety Risk AnalysisProceedings of Mensch und Computer 202210.1145/3543758.3547576(561-565)Online publication date: 4-Sep-2022
https://dl.acm.org/doi/10.1145/3543758.3547576
Pérez-Pérez MFerreira TLourenço AIgrejas GFdez-Riverola F(2022)Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representationNeurocomputing10.1016/j.neucom.2021.10.100484:C(223-237)Online publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.neucom.2021.10.100
Zhou HLi XWang CMa Y(2022)A feature selection method based on term frequency difference and positive weighting factorData & Knowledge Engineering10.1016/j.datak.2022.102060141:COnline publication date: 1-Sep-2022
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Sarkar SVinay SDjeddi CMaiti J(2022)Classification and pattern extraction of incidents: a deep learning-based approachNeural Computing and Applications10.1007/s00521-021-06780-334:17(14253-14274)Online publication date: 1-Sep-2022
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Stratogiannis GKouris PAlexandridis GSiolas GStamou GStafylopatis A(2021)Semantic enrichment of documents: a classification perspective for ontology-based imbalanced semantic descriptionsKnowledge and Information Systems10.1007/s10115-021-01615-y63:11(3001-3039)Online publication date: 1-Nov-2021
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Haris MFarfar KStocker MAuer S(2021)Federating Scholarly Infrastructures with GraphQLTowards Open and Trustworthy Digital Societies10.1007/978-3-030-91669-5_24(308-324)Online publication date: 1-Dec-2021
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Salguero ADelatorre PMedina JEspinilla MTomeu A(2019)Ontology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning TechniquesScientific Programming10.1155/2019/29172942019Online publication date: 1-Jan-2019
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