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A Hybrid Approach to Ontology Modularization

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Abstract

The design of ontologies is a non-trivial task that can simply be reduced to the reuse of one or more existing ontologies. However, since an expert in knowledge engineering would only need a part of the ontology to perform a specific task, obtaining this partition will sometimes require the modularization of existing ontologies. There exist two categories of ontology modularization techniques: partitioning and extraction. This paper describes a new hybrid modularization approach, which combines both categories in an integrated segmentation algorithm, taking advantage of the best of both worlds and capable of building ontology modules that are syntactically and semantically sufficient to achieve a given goal. The segmentation algorithm is based on the hierarchical deep and the semantic threshold, two essential parameters allowing to regulate the taxonomy path of the source ontology, and to control the proportions of the module to be built. Potentially relevant concepts are observed through semantic relationships. The approach has been implemented through a tool named COMET. A validation protocol has been defined to evaluate the quality of the different extracted modules using a number of metrics. The validation is based on a comparative study of the modules generated by COMET, compared to a given reference ontology. Many tests have been conducted one of the findings is that, the density of an ontology which represents the level of its completeness, is an essential property. From the series of tests conducted during our study, we concluded that the denser an ontology is, the denser the module returned by COMET is.

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References

  1. Leclair A, Khedri R, Marinache A. Toward measuring knowledge loss due to ontology modularization. In: Proceedings of the 11th international joint conference on knowledge discovery, knowledge engineering and knowledge management, IC3K 2019. SCITEPRESS—Science and Technology Publications, Lda; 2019 pp. 174–184. https://doi.org/10.5220/0008169301740184.

  2. De Giacomo G, Lembo D, Lenzerini M, Poggi A, Rosati R. Using ontologies for semantic data integration. In: Flesca S, Greco S, Masciari E, Saccà D, editors. A comprehensive guide through the Italian database research over the last 25 years. SBD, vol. 31. Cham: Springer; 2018. p. 187–202. https://doi.org/10.1007/978-3-319-61893-7_11.

    Chapter  Google Scholar 

  3. Xue X, Tang Z. An evolutionary algorithm-based ontology matching system. J Inf Hid Multimedia Signal Process. 2017;8(3):551–6 (High heterogeneity; Matcher combination; Matching system; Ontology matching; Optimal model; Recall and precision; Semantic correspondence; State of the art).

    Google Scholar 

  4. Khan ZC, Keet CM. Toward a framework for ontology modularity. In: The proceedings of SAICSIT'15: annual research conference on South African Institute of Computer Scientists and Information Technologists. ACM; 2015. p. 24. https://doi.org/10.1145/2815782.2815819.

  5. Algergawy A, Babalou S, Konig-Ries B. A new metric to evaluate ontology modularization. In: SumPre@ ESWC. 2016. Corpus ID: 2064828. http://ceur-ws.org/Vol-1605/paper4.pdf.

  6. Babalou S, Kargar MJ, Davarpanah SH. Large-scale ontology matching: a review of the literature. In: Web research (ICWR), 2016 second international conference on. IEEE; 2016. pp. 158–165. https://doi.org/10.1109/ICWR.2016.7498461.

  7. Movaghati MA, Barforoush AA. Modular-based measuring semantic quality of ontology. In: Computer and knowledge engineering (ICCKE), 2016 6th international conference on. IEEE; 2016. pp. 13–18. https://doi.org/10.1109/ICCKE.2016.7802108.

  8. Palmisano I, Tamma V, Payne T, Doran P, et al. Task oriented evaluation of module extraction techniques. In: Bernstein A, et al., editors. The semantic web—ISWC 2009. ISWC 2009. Lecture notes in computer science, vol. 5823. Berlin: Springer; 2009. https://doi.org/10.1007/978-3-642-04930-9_9.

    Chapter  Google Scholar 

  9. Stuckenschmidt H, Klein M. Structure-based partitioning of large concept hierarchies. In: Mcilraith SA, Plexousakis D, van Harmelen F, editors. The semantic web—ISWC 2004. ISWC 2004. Lecture notes in computer science, vol. 3298. Berlin: Springer; 2004. https://doi.org/10.1007/978-3-540-30475-3_21.

    Chapter  Google Scholar 

  10. Grau BC, Parsia B, Sirin E, Kalyanpur A. Automatic partitioning of owl ontologies using e-connections. In: Proceedings of the 2005 international workshop on description logics (DL-2005). 2005.

  11. Algergawy A, Babalou S, Klan F, et al. Ontology modularization with OAPT. J Data Semant. 2020;9:53–83. https://doi.org/10.1007/s13740-020-00114-7.

    Article  Google Scholar 

  12. Dupont P, Callut J, Dooms G, Monette J, Deville Y. Relevant subgraph extraction from random walks in a graph. Technical report, Catholic University of Louvain, UCL/INGI (2006). Corpus ID: 15222702

  13. Jiang JJ, Conrath DW. Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of 10th international conference on research in computational linguistics (ROCLING X), computing research repository CoRR. 1997. pp. 19–33. https://aclanthology.org/O97-1002.pdf.

  14. Shi G, Wu Y, Liu J, Wan S, Wang W, Lu T. Incremental few-shot semantic segmentation via embedding adaptive-update and hyper-class representation. In: Proceedings of the 30th ACM international conference on multimedia, 2022. https://doi.org/10.1109/TNSE.2022.3151502.

  15. Ghosh M, Abdulrab H, Naja H, Khalil M. Ontology learning process as a bottom-up strategy for building domain-specific ontology from legal texts. In :proceedings of 9th international conference on agents and artificial intelligence. 2017. pp 473–480. https://doi.org/10.5220/0006188004730480.

  16. Stuckenschmidt H, Schlicht A. Structure-based partitioning of large ontologies. Dans Modular Ontologies, volume 5445 de Lecture notes in computer science. Springer; 2009. pp. 187–210. https://doi.org/10.1007/978-3-642-01907-4_9.

  17. Wu Z, Palmer M. Verbs semantics and lexical selection. In: Proceedings of the 32nd annual meeting on association for computational linguistics, association for computational linguistics. 1994. pp. 133–138. https://doi.org/10.3115/981732.981751.

  18. Dellschaft K, Staab S. On how to perform a gold standard-based evaluation of ontology learning. In: The semantic web-ISWC 2006. Springer; 2006. pp. 228–241. https://doi.org/10.1007/11926078_17.

  19. Alani H, Brewster C, Shadbolt N. Ranking ontologies with aktiverank. In: Proceedings of the 5th international conference on the semantic web (ISWC’06). Springer; 2006; pp. 1–15. https://doi.org/10.1007/11926078_1.

  20. Ghosh M, Abdulrab H, Naja H, Khalil M. Using the unified foundational ontology (UFO) for grounding legal domain ontologies. In: Proceedings of 9th international conference on knowledge engineering and ontology development. Madeira, Portugal; 2017. Hal-01644015.

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Authors and Affiliations

  1. National Advanced School of Engineering, University of Yaoundé 1, Yaoundé, Cameroon

    Bernabé Batchakui & Roger Nkambou

  2. Department of Computer Science, University of Quebec at Montreal, Montreal, Canada

    Emile Tawamba

  3. University Institute of Technology, University of Douala, Douala, Cameroon

    Emile Tawamba

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  1. Bernabé Batchakui
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  2. Roger Nkambou
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  3. Emile Tawamba
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Correspondence to Bernabé Batchakui.

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This article is part of the topical collection “Knowledge Discovery, Knowledge Engineering and Knowledge Management 2021” guest edited by Joaquim Filipe, Ana Fred, Jorge Bernardino and Elio Masciari.

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Batchakui, B., Nkambou, R. & Tawamba, E. A Hybrid Approach to Ontology Modularization. SN COMPUT. SCI. 4, 634 (2023). https://doi.org/10.1007/s42979-023-02066-8

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