Ontology Mapping and Rule-Based Inference for Learning Resource Integration

  • Jetinai, Kotchakorn ;
  • Arch-int, Ngamnij ;
  • Arch-int, Somjit
  • Received : 2016.04.11
  • Accepted : 2016.04.28
  • Published : 2016.06.30


With the increasing demand for interoperability among existing learning resource systems in order to enable the sharing of learning resources, such resources need to be annotated with ontologies that use different metadata standards. These different ontologies must be reconciled through ontology mediation, so as to cope with information heterogeneity problems, such as semantic and structural conflicts. In this paper, we propose an ontology-mapping technique using Semantic Web Rule Language (SWRL) to generate semantic mapping rules that integrate learning resources from different systems and that cope with semantic and structural conflicts. Reasoning rules are defined to support a semantic search for heterogeneous learning resources, which are deduced by rule-based inference. Experimental results demonstrate that the proposed approach enables the integration of learning resources originating from multiple sources and helps users to search across heterogeneous learning resource systems.


Conflict resolution;Learning resources;Ontology mapping;Rule-based inference


  1. H. Lu and Q. Z. Li, “Ontology based resolution of semantic conflicts in information integration,” Wuhan University Journal of Natural Sciences, vol. 9, no. 5, pp. 606-610, 2004.
  2. M. M. Brut, F. Sedes, and S. D. Dumitrescu, “A semantic-oriented approach for organizing and developing annotation for e-learning,” IEEE Transactions on Learning Technologies, vol. 4, no. 3, pp. 239-248, 2011.
  3. M. P. Cuellar, M. Delgado, and M. C. Pegalajar, “A common framework for information sharing in e-learning management systems,” Expert Systems with Applications, vol. 38, no. 3, pp. 2260-2270, 2011.
  4. D. Gasevic and M. Hatala, “Ontology mappings to improve learning resource search,” British Journal of Educational Technology, vol. 37, no. 3, pp. 375-389, 2006.
  5. A. Bouzeghoub and A. Elbyed, “Ontology mapping for web-based educational systems interoperability,” Interoperability in Business Information Systems, vol. 1, no. 1, pp. 73-84, 2006.
  6. S. Ram and J. Park, “Semantic conflict resolution ontology (SCROL): an ontology for detecting and resolving data and schema-level semantic conflicts,” IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 2, pp. 189-202, 2004.
  7. K. Banlue, N. Arch-int, and S. Arch-int, "Ontology-based metadata integration approach for learning resource interoperability," in Proceeding of the 6th International Conference on Semantics Knowledge and Grid, Ningbo, China, pp. 195-202, 2010.
  8. N. Arch-int and S. Arch-int, “Semantic ontology mapping for interoperability of learning resource systems using a rule-based reasoning approach,” Expert Systems with Applications, vol. 40, no. 18, pp. 7428-7443, 2013.
  9. K. Jetinai, N. Arch-int, W. Rungworawut, and S. Arch-int, “Ontology reconciliation for learning resource interoperability,” International Journal of Digital Content Technology & Its Applications, vol. 7, no. 6, pp. 191-200, 2013.
  10. C. F. Naiman and A. M. Ouksel, “A classification of semantic conflicts in heterogeneous database systems,” Journal of Organizational Computing, vol. 5, no. 2, pp. 167-193, 1995.
  11. V. Kashyap and A. Sheth, “Semantic and schematic similarities between database objects: a context-based approach,” The VLDB Journal, vol. 5, no. 4, pp. 276-304, 1996.
  12. S. Kaza and H. Chen, “Evaluating ontology mapping techniques: an experiment in public safety information sharing,” Decision Support Systems, vol. 45, no. 4, pp. 714-728, 2008.
  13. G. A. Miller, R. Beckwith, C. Fellbaum, D. Gross, and K. J. Miller, “Introduction to WordNet: an on-line lexical database,” International Journal of Lexicography, vol. 3, no. 4, pp. 235-244, 1990.
  14. Z. Wu, and M. Palmer, "Verbs semantics and lexical selection," in Proceeding of the 32nd Annual Meeting on Association for Computational Linguistics, Las Cruces, NM, pp. 133-138, 1994.
  15. J. Euzenat, "Semantic precision and recall for ontology alignment evaluation," in Proceeding of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, pp. 348-353, 2007.