SAFE NUSANTARA: A semi-automatic framework for engineering and populating a Nusantara Food Ontology

Authors

  • Kemas Rahmat Saleh Wiharja Telkom University
  • Mohamad Hardyman Barawi Universiti Malaysia Sarawak
  • Ade Romadhony Telkom University
  • Imelda Atastina Telkom University
  • Ramanti Dharayani Telkom University
  • Mohd Kamal Othman Universiti Malaysia Sarawak

DOI:

https://doi.org/10.21108/ijoict.v10i2.1042

Keywords:

Artificial intelligence, Knowledge representation and reasoning, Ontology Engineering

Abstract

Constructing a comprehensive food ontology, particularly for culturally diverse cuisines like Southeast East Asian (Nusantara), is hindered by the variability of online recipes and the scarcity of structured data. This research introduces SAFE Nusantara, a novel semi-automated system designed to build and populate a Nusantara food ontology by extracting relevant terms from diverse online sources in Indonesian and Malaysian languages. By leveraging a combination of techniques, including topic modelling, natural language processing, and knowledge graph techniques, SAFE Nusantara addresses the challenges of data format diversity and language specificity. The system has demonstrated significant improvements in the accuracy of food classification and has the potential to enhance food recommendation systems and cultural heritage preservation efforts.

Downloads

Download data is not yet available.

References

[1] N. Anas, P. D. E. A. Z. Engku Alwi, Z. Yaacob, A. Ramlan, A. H. Ishak, and S. Ab Manan, “THE NUSANTARA ETHNIC GROUPS FROM HISTORICAL AND GENOMIC PERSPECTIVES,” International Journal of Civil Engineering and Technology, vol. 10, pp. 287–301, 2019.

[2] K. Sukenti, “Gastronomy Tourism in Several Neighbor Countries of Indonesia: a Brief Review,” Journal of Indonesian Tourism and Development Studies, vol. 2, no. 2, pp. 55–63, 2014, doi: 10.21776/ub.jitode.2014.002.02.03.

[3] Y.-C. Lin, T. Pearson, and L. Cai, “Food as a form of Destination Identity: A Tourism Destination Brand Perspective,” Tourism and Hospitality Research, vol. 11, 2011, doi: 10.1057/thr.2010.22.

[4] D. Timothy and A. Ron, “Understanding heritage cuisines and tourism: Identity, image, authenticity, and change,” Journal of Heritage Tourism, vol. 8, pp. 99–104, 2013, doi: 10.1080/1743873X.2013.767818.

[5] A.-L. Barabási, G. Menichetti, and J. Loscalzo, “The unmapped chemical complexity of our diet,” Nat Food, vol. 1, no. 1, pp. 33–37, 2020, doi: 10.1038/s43016-019-0005-1.

[6] D. Dooley et al., “FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration,” NPJ Sci Food, vol. 2, 2018, doi: 10.1038/s41538-018-0032-6.

[7] R. A. Harrington, V. Adhikari, M. Rayner, and P. Scarborough, “Nutrient composition databases in the age of big data: foodDB, a comprehensive, real-time database infrastructure.,” BMJ Open, vol. 9, no. 6, p. e026652, Jun. 2019, doi: 10.1136/bmjopen-2018-026652.

[8] H. Seed, “Us department of agriculture agricultural research service.” Accessed: May 07, 2020. [Online]. Available: fdc. nal. usda. gov/fdc-app. html

[9] A. Møller and J. Ireland, “LanguaLTM 2017 - Thesaurus,” 2018. doi: 10.13140/RG.2.2.23131.26404.

[10] H. Putra, R. Mahendra, and F. Darari, “BudayaKB: Extraction of Cultural Heritage Entities from Heterogeneous Formats,” 2019, pp. 1–9. doi: 10.1145/3326467.3326487.

[11] T. Declerck, A. G. Pérez, O. Vela, Z. Gantner, and D. Manzano-Macho, “Multilingual Lexical Semantic Resources for Ontology Translation,” in Proceedings of the Fifth International Conference on Language Resources and Evaluation ({LREC}{’}06), N. Calzolari, K. Choukri, A. Gangemi, B. Maegaard, J. Mariani, J. Odijk, and D. Tapias, Eds., Genoa, Italy: European Language Resources Association (ELRA), May 2006. [Online]. Available: http://www.lrec-conf.org/proceedings/lrec2006/pdf/397_pdf.pdf

[12] N. H. B. M. Noor, S. Sapuan, and F. Bond, “Creating the Open {W}ordnet {B}ahasa,” in Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation, H. H. Gao and M. Dong, Eds., Singapore: Institute of Digital Enhancement of Cognitive Processing, Waseda University, Dec. 2011, pp. 255–264. [Online]. Available: https://aclanthology.org/Y11-1027

[13] A. Bramantoro and I. Syamsuddin, “Melayu Grid: Sharing Language Service to Bridge Cultural Difference,” 2020, pp. 1–6. doi: 10.1109/ICCIS49240.2020.9257691.

[14] S. Mishra Tiwari and S. Jain, “A Study of Various Approaches and Tools on Ontology,” Proceedings - 2015 IEEE International Conference on Computational Intelligence and Communication Technology, CICT 2015, pp. 57–61, 2015, doi: 10.1109/CICT.2015.43.

[15] F. N. Al-Aswadi, H. Y. Chan, and K. H. Gan, “Automatic ontology construction from text: a review from shallow to deep learning trend,” Artif Intell Rev, vol. 53, no. 6, pp. 3901–3928, 2020, doi: 10.1007/s10462-019-09782-9.

[16] O. Maimon and A. Browarnik, “Ontology Learning from Text: Why the Ontology Learning Layer Cake is not Viable,” Int. J. Signs Semiot. Syst., vol. 4, no. 2, pp. 1–14, Jul. 2015, doi: 10.4018/IJSSS.2015070101.

[17] C. W. Y. Wong, K. Lai, K.-C. Shang, C.-S. Lu, and T. K. P. Leung, “Green operations and the moderating role of environmental management capability of suppliers on manufacturing firm performance,” Int J Prod Econ, vol. 140, no. 1, pp. 283–294, 2012, doi: https://doi.org/10.1016/j.ijpe.2011.08.031.

[18] A. Konys, “Knowledge systematization for ontology learning methods,” Procedia Comput Sci, vol. 126, pp. 2194–2207, 2018, doi: 10.1016/j.procs.2018.07.229.

[19] N. Noy and D. Mcguinness, “Ontology Development 101: A Guide to Creating Your First Ontology,” Knowledge Systems Laboratory, vol. 32, 2001.

[20] D. M. Blei, “Probabilistic topic models,” Commun. ACM, vol. 55, no. 4, pp. 77–84, Apr. 2012, doi: 10.1145/2133806.2133826.

[21] M. Barawi, C. Lin, and A. Siddharthan, “Automatically Labelling Sentiment-Bearing Topics with Descriptive Sentence Labels,” Nov. 2017, pp. 299–312. doi: 10.1007/978-3-319-59569-6_38.

[22] T. Ivanova, “ONTOLOGY LEARNING TECHNOLOGIES – BRIEF SURVEY, TRENDS AND PROBLEMS,” 2012.

[23] A. Alarfaj and A. S. Al-Salman, “Ontology Construction from Text : Challenges and Trends,” 2015. [Online]. Available: https://api.semanticscholar.org/CorpusID:174796250

[24] M. Rani, A. Dhar, and O. Vyas, “Semi-Automatic Terminology Ontology Learning Based on Topic Modeling,” Eng Appl Artif Intell, vol. 63, pp. 108–125, 2017, doi: 10.1016/j.engappai.2017.05.006.

[25] R. Lourdusamy and S. Abraham, “A Survey on Methods of Ontology Learning from Text,” 2020, pp. 113–123. doi: 10.1007/978-3-030-38501-9_11.

[26] M. Steyvers and T. Griffiths, “Probabilistic Topic Models.”

[27] A. Faatz and R. Steinmetz, “Ontology Enrichment with Texts from the WWW,” 2002.

[28] N. F. Noy and M. Klein, “Ontology Evolution: Not the Same as Schema Evolution,” Knowl Inf Syst, vol. 6, no. 4, pp. 428–440, 2004, doi: 10.1007/s10115-003-0137-2.

[29] G. Petasis, V. Karkaletsis, G. Paliouras, A. Krithara, and E. Zavitsanos, “Ontology Population and Enrichment: State of the Art,” 2011, pp. 134–166. doi: 10.1007/978-3-642-20795-2_6.

[30] V. Iyer, L. Mohan, M. Bhatia, and Y. R. Reddy, “A Survey on Ontology Enrichment from Text,” in Proceedings of the 16th International Conference on Natural Language Processing, D. M. Sharma and P. Bhattacharya, Eds., International Institute of Information Technology, Hyderabad, India: NLP Association of India, Dec. 2019, pp. 95–104. [Online]. Available: https://aclanthology.org/2019.icon-1.11

[31] L. Sanagavarapu, V. Iyer, and R. Reddy, “OntoEnricher: A Deep Learning Approach for Ontology Enrichment from Unstructured Text,” 2021. doi: 10.48550/arXiv.2102.04081.

[32] R. Idoudi, K. Ettabaa, B. Solaiman, and K. Hamrouni, “Ontology knowledge mining for ontology conceptual enrichment,” Knowledge Management Research & Practice, vol. 17, pp. 1–10, 2018, doi: 10.1080/14778238.2018.1538599.

[33] R. Maimone, T. Bailoni, C. Eccher, M. Dragoni, and M. Guerini, “Healthy Lifestyle Support: The PerKApp Ontology,” 2016. doi: 10.1007/978-3-319-54627-8_2.

[34] J. Pathak, N. Koul, D. Caragea, and V. G. Honavar, “A framework for semantic web services discovery,” in Proceedings of the 7th Annual ACM International Workshop on Web Information and Data Management, in WIDM ’05. New York, NY, USA: Association for Computing Machinery, 2005, pp. 45–50. doi: 10.1145/1097047.1097057.

[35] A. Browarnik and O. Maimon, “Ontology Learning from Text:,” International Journal of Signs and Semiotic Systems, vol. 4, pp. 1–14, Jun. 2015, doi: 10.4018/IJSSS.2015070101.

Downloads

Published

2025-05-23

How to Cite

Wiharja, K. R. S., Barawi, M. H., Romadhony, A., Atastina, I., Dharayani, R., & Othman, M. K. (2025). SAFE NUSANTARA: A semi-automatic framework for engineering and populating a Nusantara Food Ontology. International Journal on Information and Communication Technology (IJoICT), 10(2), 321–337. https://doi.org/10.21108/ijoict.v10i2.1042

Issue

Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024

Section

Data Science

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Manuscript submitted to IJoICT has to be an original work of the author(s), contains no element of plagiarism, and has never been published or is not being considered for publication in other journals. Author(s) shall agree to assign all copyright of published article to IJoICT. Requests related to future re-use and re-publication of major or substantial parts of the article must be consulted with the editors of IJoICT.