research-article

Instance-based ontology matching for e-learning material using an associative pattern classifier

Authors:

Sergio Cern-Figueroa,

Itzam Lpez-Yez,

Wadee Alhalabi,

Oscar Camacho-Nieto,

Yenny Villuendas-Rey,

Mario Aldape-Prez,

Cornelio Yez-MrquezAuthors Info & Claims

Computers in Human Behavior, Volume 69, Issue C

Pages 218 - 225

https://doi.org/10.1016/j.chb.2016.12.039

Published: 01 April 2017 Publication History

Abstract

The present work describes a new model of pattern classification and its application to align instances from different ontologies, which are in turn related to e-learning educative content in a Knowledge Society context. In general, ontologies are the fundamental tool inherent to Semantic Web. In particular, the problem of ontology matching is modeled in this paper as a binary pattern classification problem. The original model presented here was validated through experiments, which were done on data taken from the OAEI (Ontology Alignment Evaluation Initiative) 2014 campaign, presented in the OWL (Web Ontology Language) format, as well as on data taken from two international repositories, ADRIADNE and MERLOT, in LOM (Learning Objects Metadata) format. The results obtained show a high precision measurement when compared against some of the best methods present in the state of the art. A new model for ontology matching over educative content repositories is introduced.The model was validated on the OAEI 2014 campaign, ADRIADNE and MERLOT.The homogeneity of resources for e-learning is improved.The results obtained show a high precision measurement.

References

[1]

M. Abramowitz, I.A. Stegun, Handbook of mathematical functions with formulas, graphs and mathematical tables, Dover Publications, New York, 1972.

Digital Library

[2]

S.B. Aher, L.M.R.J. Lobo, Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data, Knowledge-Based Systems, 51 (2013) 1-14.

Digital Library

[3]

J. Atkinson, A. Gonzalez, M. Munoz, H. Astudillo, Web metadata extraction and semantic indexing for learning objects extraction, Applied Intelligence, 41 (2014) 649-664.

Digital Library

[4]

T. Berners-Lee, R. Cailliau, J.-F. Groff, B. Pollermann, World-Wide Web: An information infrastructure for high-energy phsyics, in: Software engineering, articicial inteligence and expert systems for high energy and nuclear physics, World Scientific, Singapore, 1992, pp. 157-164.

[5]

T. Berners-Lee, J. Hendler, O. Lassila, The semantic web, Scientific American, 284 (2001) 34-43.

[6]

G. Beydoun, Formal concept analysis for an e-learning semantic web, Expert Systems with Applications, 36 (2009) 10952-10961.

Digital Library

[7]

M.L. Caliusco, G. Stegmayer, Semantic web technologies and artificial neural networks for intelligent web knowledge source discovery, in: Emergent web Intelligence: Advanced semantic technologies, Springer, London, 2010, pp. 17-36.

[8]

RDF 1.1 concepts and abstract syntax: W3C recommendation, in: RDF 1.1 concepts and abstract syntax: W3C recommendation, 2014. http://www.w3.org

[9]

Z. Dragisic, K. Eckert, J. Euzenat, D. Faria, A. Ferrara, R. Granada, Results of the ontology alignment evaluation initiative 2014, in: Proceedings of the 9th international workshop on ontology matching collocated with the 13th international semantic web conference (ISWC 2014), OAEI, Riva del Garda, Trentino, Italy, 2014, pp. 61-104.

Digital Library

[10]

A. Gladun, J. Rogushina, F. Garca-Sanchez, R. Martnez-Bjar, J.T. Fernndez-Breis, An application of intelligent techniques and Semantic Web technologies in e-learning environments, Expert Systems with Applications, 36 (2009) 1922-1931.

Digital Library

[11]

S.I. Grossman, J.J.F. Godoy, lgebra lineal, McGraw-Hill, Mxico, 2012.

[12]

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I.H. Witten, The WEKA data mining Software: An update, SIGKDD explorations newsletter, 11 (2009) 10-18.

Digital Library

[13]

B.B. Hariri, H. Abolhassani, H. Sayyadi, Aneural-networks-based approach for ontology alignment, in: Proceedings of the joint 3rd international conference on soft computing and intelligent systems and 7th international symposium on advanced intelligent systems (SCIS & ISIS 2006), Japan Society for Fuzzy Theory and Intelligent Informatics, Tokyo, Japan, 2006, pp. 1248-1252.

[14]

M. Houshmand, M. Naghibzadeh, S. Araban, Reliability-based similarity aggregation in ontology matching, in: Proceedings of the IEEE international conference on intelligent computing and intelligent systems (ICIS 2010), IEEE, Xiamen: China, 2010, pp. 744-749.

[15]

Institute of Electrical and Electronics Engineers, IEEE standard for learning object metadata (IEEE Std 1484.12.12002), 2002. http://ieeexplore.ieee.org/iel5/8032/22180/01032843.pdf

[16]

D.R. Karger, The semantic web and end users: What's wrong and how to fix it, Internet Computing, IEEE, 18 (2014) 64-70.

[17]

R. Kohavi, Astudy of cross-validation and bootstrap for accuracy estimation and model selection, in: Proceedings of the fourteenth international joint conference on artificial intelligence, Morgan Kaufmann, Montreal, Quebec, Canada, 1995, pp. 1137-1145.

Digital Library

[18]

M. Konstantinos, M. Penelope, K. Giannis, L.-T. Aglaia, Semantic e-Learning: Next generation of e-Learning?, in: Next generation society. Technological and legal issues, Springer, Berlin Heidelberg, 2010, pp. 295-308.

[19]

A. Lawrynowicz, J. Potoniec, Pattern based feature construction in semantic data mining, International Journal on Semantic Web and Information Systems (IJSWIS), 10 (2014) 27-65.

Digital Library

[20]

J. Liu, L. Qin, H. Wang, An ontology mapping method based on support vector machine, in: Proceedings of the 8th international workshop on ontology matching collocated with the 12th international semantic web conference (ISWC 2013), OAEI, Sydney, Australia, 2013, pp. 225-226.

Digital Library

[21]

L. Liu, F. Yang, P. Zhang, J.-Y. Wu, L. Hu, SVM-based ontology matching approach, International Journal of Automation and Computing, 9 (2012) 306-314.

Digital Library

[22]

Z. Li, W. Xu, L. Zhang, R.Y.K. Lau, An ontology-based Web mining method for unemployment rate prediction, Decision Support Systems, 66 (2014) 114-122.

Digital Library

[23]

N. Loutas, V. Peristeras, K. Tarabanis, Towards a reference service model for the Web of Services, Data & Knowledge Engineering, 70 (2011) 753-774.

Digital Library

[24]

J.M. Luna, C. Romero, J.R. Romero, S. Ventura, An evolutionary algorithm for the discovery of rare class association rules in learning management systems, Applied Intelligence, 42 (2015) 501-513.

Digital Library

[25]

M.D. Lytras, P.O. de Pablos, Software technologies in knowledge society, Journal of Universal Computer Science, 17 (2011) 1219-1221.

[26]

M.D. Lytras, A. Naeve, Intelligent learning infrastructure for knowledge intensive organizations: A semantic web perspective, IGI Global, Hershey, PA, 2005.

[27]

M. Mao, Y. Peng, M. Spring, Ontology mapping: As a binary classification problem, Concurrency and Computation: Practice and Experience, 23 (2011) 1010-1025.

Digital Library

[28]

V. Mascardi, A. Locoro, P. Rosso, Automatic ontology matching via upper ontologies: A systematic evaluation, IEEE Transactions on Knowledge and Data Engineering, 22 (2010) 609-623.

Digital Library

[29]

OWL web ontology language overview: W3C recommendation, in: OWL web ontology language overview: W3C recommendation, 2004. http://www.w3.org

[30]

O. Mohammed, R. Benlamri, Developing a semantic web model for medical differential diagnosis recommendation, Journal of Medical Systems, 38 (2014) 1-18.

Digital Library

[31]

A. Muoz, J. Lasheras, A. Capel, M. Cantabella, A. Caballero, OntoSakai: On the optimization of a Learning Management System using semantics and user profiling, Expert Systems with Applications, 42 (2015) 5995-6007.

Digital Library

[32]

O. Mustapaa, A. Karahoca, D. Karahoca, H. Uzunboylu, Hello world, web mining for e-learning, Procedia Computer Science, 3 (2011) 1381-1387.

[33]

H. Nacer, D. Aissani, Semantic web services: Standards, applications, challenges and solutions, Journal of Network and Computer Applications, 44 (2014) 134-151.

Digital Library

[34]

L. Otero-Cerdeira, F.J. Rodrguez-Martnez, A. Gmez-Rodrguez, Ontology matching: A literature review, Expert Systems with Applications, 42 (2015) 949-971.

Digital Library

[35]

K. Pancerz, A. Lewicki, Encoding symbolic features in simple decision systems over ontological graphs for PSO and neural network based classifiers, Neurocomputing, 144 (2014) 338-345.

Digital Library

[36]

I. Pealver-Martinez, F. Garcia-Sanchez, R. Valencia-Garcia, M. Rodrguez-Garca, V. Moreno, A. Fraga, Feature-based opinion mining through ontologies, Expert Systems with Applications, 41 (2014) 5995-6008.

[37]

M. Pietranik, N.T. Nguyen, Amulti-attribute based framework for ontology aligning, Neurocomputing, 146 (2014) 276-290.

Digital Library

[38]

R. Rezaei, T.K. Chiew, S.P. Lee, Z. Shams Aliee, Asemantic interoperability framework for software as a service systems in cloud computing environments, Expert Systems with Applications, 41 (2014) 5751-5770.

[39]

M. Rubiolo, M.L. Caliusco, G. Stegmayer, M. Coronel, M. Gareli Fabrizi, Knowledge discovery through ontology Matching: An approach based on an Artificial Neural Network model, Information Sciences, 194 (2012) 107-119.

Digital Library

[40]

B. Schopman, S. Wang, A. Isaac, S. Schlobach, Instance-based ontology matching by instance enrichment, Journal on Data Semantics, 1 (2012) 219-236.

[41]

P. Shvaiko, J. Euzenat, Ontology matching: State of the art and future challenges, IEEE Transactions on Knowledge and Data Engineering, 25 (2013) 158-176.

Digital Library

[42]

G. Stumme, A. Hotho, B. Berendt, Semantic web mining: State of the art and future directions, Web Semantics: Science, Services and Agents on the World Wide Web, 4 (2006) 124-143.

Digital Library

[43]

J. Tang, J. Li, B. Liang, X. Huang, Y. Li, K. Wang, Using Bayesian decision for ontology mapping, Web Semantics: Science, Services and Agents on the World Wide Web, 4 (2006) 243-262.

Digital Library

[44]

M. Thangaraj, G. Sujatha, An architectural design for effective information retrieval in semantic web, Expert Systems with Applications, 41 (2014) 8225-8233.

[45]

A.V. Uriarte-Arcia, C. Yez-Mrquez, J. Gama, I. Lpez-Yez, O. Camacho-Nieto, Data stream classification based on the gamma classifier, Mathematical Problems in Engineering (2015).

[46]

J.C. Vidal, M. Lama, E. Otero-Garca, A. Bugarn, Graph-based semantic annotation for enriching educational content with linked data, Knowledge-Based Systems, 55 (2014) 29-42.

Digital Library

[47]

J. Wang, J. Peng, O. Liu, Aclassification approach for less popular webpages based on latent semantic analysis and rough set model, Expert Systems with Applications, 42 (2015) 642-648.

Digital Library

[48]

K. Yang, R. Steele, Ontology mapping based on concept classification, in: 3rd IEEE international conference on digital ecosystems and technologies 2009 (DEST09), IEEE, Istanbul, Turkey, 2009, pp. 656-661.

[49]

X. Yang, D. Wallom, S. Waddington, J. Wang, A. Shaon, B. Matthews, Cloud computing in e-science: Research challenges and opportunities, The Journal of Supercomputing, 70 (2014) 408-464.

Digital Library

Cited By

George GLal A(2024)PERKC: Personalized kNN With CPT for Course Recommendations in Higher EducationIEEE Transactions on Learning Technologies10.1109/TLT.2023.334664517(885-892)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TLT.2023.3346645
Djenouri YSrivastava GBelhadi ALin J(2024)Intelligent blockchain management for distributed knowledge graphs in IoT 5G environmentsTransactions on Emerging Telecommunications Technologies10.1002/ett.433235:4Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1002/ett.4332
Belhadi ADjenouri YSrivastava GLin J(2023)Fast and Accurate Framework for Ontology Matching in Web of ThingsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/357870822:5(1-19)Online publication date: 17-Jan-2023
https://dl.acm.org/doi/10.1145/3578708
Show More Cited By

Instance-based ontology matching for e-learning material using an associative pattern classifier
1. Computing methodologies

Recommendations

Axiom-based ontology matching
K-CAP '05: Proceedings of the 3rd international conference on Knowledge capture

This paper introduces a new approach of ontology matching named axiom-based ontology matching. As this approach is founded on the use of axioms, it is mainly dedicated to heavyweight ontology, but it can also be applied to lightweight ontology as a ...
A New Instance-Based Approach for Ontology Alignment

Due to the increasing number of information sources available on the web and their distribution and heterogeneity, ontology alignment became a very important and inevitable problem to resolve in order to ensure semantic interoperability between these ...
Mapping the central LOD ontologies to PROTON upper-level ontology
OM'10: Proceedings of the 5th International Conference on Ontology Matching - Volume 689

Linking Open Data (LOD) facilitates the emergence of a web of linked data by publishing and interlinking open data on the web in RDF. One can explore linked data across servers by following the links in the graph. The LOD cloud has 203 datasets and more ...

Comments

Information & Contributors

Information

Published In

cover image Computers in Human Behavior

Computers in Human Behavior Volume 69, Issue C

April 2017

454 pages

ISSN:0747-5632

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 April 2017

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

George GLal A(2024)PERKC: Personalized kNN With CPT for Course Recommendations in Higher EducationIEEE Transactions on Learning Technologies10.1109/TLT.2023.334664517(885-892)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TLT.2023.3346645
Djenouri YSrivastava GBelhadi ALin J(2024)Intelligent blockchain management for distributed knowledge graphs in IoT 5G environmentsTransactions on Emerging Telecommunications Technologies10.1002/ett.433235:4Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1002/ett.4332
Belhadi ADjenouri YSrivastava GLin J(2023)Fast and Accurate Framework for Ontology Matching in Web of ThingsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/357870822:5(1-19)Online publication date: 17-Jan-2023
https://dl.acm.org/doi/10.1145/3578708
Tahir SHafeez YAbbas MNawaz AHamid B(2022)Smart Learning Objects Retrieval for E-Learning with Contextual Recommendation based on Collaborative FilteringEducation and Information Technologies10.1007/s10639-022-10966-027:6(8631-8668)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s10639-022-10966-0
Singh MAdebayo SSaini MSingh J(2021)Indian government E-learning initiatives in response to COVID-19 crisis: A case study on online learning in Indian higher education systemEducation and Information Technologies10.1007/s10639-021-10585-126:6(7569-7607)Online publication date: 23-Jun-2021
https://dl.acm.org/doi/10.1007/s10639-021-10585-1
Yáñez-Márquez CLópez-Yáñez IAldape-Pérez MCamacho-Nieto OArgüelles-Cruz AVilluendas-Rey Y(2018)Theoretical Foundations for the Alpha-Beta Associative MemoriesNeural Processing Letters10.5555/3288065.328812848:2(811-847)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.5555/3288065.3288128
Song SZhang XQin G(2017)Multi-domain ontology mapping based on semanticsCluster Computing10.1007/s10586-017-1087-x20:4(3379-3391)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s10586-017-1087-x

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents