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Research Article

Ontology-based semantic data interestingness using BERT models

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Article: 2190499 | Received 18 Dec 2022, Accepted 09 Mar 2023, Published online: 11 Apr 2023

References

  • Abas, A. R., El-Henawy, I., Mohamed, H., & Abdellatif, A. (2020). Deep learning model for fine-grained aspect-based opinion mining. IEEE Access, 8, 128845–128855. https://doi.org/10.1109/Access.6287639
  • Abhilash, C., & Mahesh, K. (2021). Graph analytics applied to COVID19 karnataka state dataset. In 2021 The 4th International Conference on Information Science and Systems (pp. 74–80). Association for Computing Machinery.doi:10.1145/3459955.3460603
  • Abhilash, C. B., & Mahesh, K. (2022a). Ontology-based interestingness in covid-19 data. In Metadata and Semantic Research: 15th International Conference, MTSR 2021, Virtual Event, November 29–December 3, 2021, Revised Selected Papers (pp. 322–335). Springer.
  • Abhilash, C. B., & Mahesh, K. (2022b). Ontology-based method for semantic association rules. In IEEE 19th India Council International Conference (pp. 1–7). IEEE.
  • Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings 20th International Conference Very Large Data Bases, VLDB (Vol. 1215, pp. 487–499). Citeseer.
  • Alzubi, J. A., Jain, R., Singh, A., Parwekar, P., & Gupta, M. (2021). COBERT: COVID-19 question answering system using BERT. Arabian Journal for Science and Engineering, 1–11.
  • Apostolopoulos, I. D., & Mpesiana, T. A. (2020). Covid-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 43(2), 635–640. https://doi.org/10.1007/s13246-020-00865-4
  • Arora, P., Kumar, H., & Panigrahi, B. K. (2020). Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India. Chaos, Solitons & Fractals, 139, 110017. https://doi.org/10.1016/j.chaos.2020.110017
  • Bellandi, A., Furletti, B., Grossi, V., & Romei, A. (2007). Ontology-driven association rule extraction: A case study. Contexts and Ontologies Representation and Reasoning, 10.
  • Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., & Hellmann, S. (2009). Dbpedia-a crystallization point for the web of data. Journal of Web Semantics, 7(3), 154–165. https://doi.org/10.1016/j.websem.2009.07.002
  • Bringmann, B., Nijssen, S., & Zimmermann, A. (2011). Pattern-based classification: A unifying perspective. arXiv preprint arXiv:1111.6191.
  • Çelik Ertuğrul, D., & Ulusoy, D. C. (2022). A knowledge-based self-pre-diagnosis system to predict covid-19 in smartphone users using personal data and observed symptoms. Expert Systems, 39(3), e12716.
  • Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.t., Choi, Y., Liang, P., & Zettlemoyer, L. (2018). QuAC: Question answering in context. arXiv preprint arXiv:1808.07036.
  • Ciotti, F., & Tomasi, F. (2016). Formal ontologies, linked data, and TEI semantics. Journal of the Text Encoding Initiative, 9.
  • Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
  • Gahar, R. M., Arfaoui, O., Hidri, M. S., & Hadj-Alouane, N. B. (2018). An ontology-driven mapreduce framework for association rules mining in massive data. Procedia Computer Science, 126, 224–233. https://doi.org/10.1016/j.procs.2018.07.236
  • Garbe, W. (2012). SymSpell (Vol. 6). https://github.com/wolfgarbe/SymSpell
  • Guo, X., Mirzaalian, H., Sabir, E., Jaiswal, A., & Abd-Almageed, W. (2020). Cord19sts: Covid-19 semantic textual similarity dataset. arXiv preprint arXiv:2007.02461.
  • Mangla, M., & Akhare, R. (2015). Association rules filtration using dynamic methods. International Research Journal of Engineering and Technology, 2(3), 1103–1106.
  • Marinica, C., & Guillet, F. (2010). Knowledge-based interactive postmining of association rules using ontologies. IEEE Transactions on Knowledge and Data Engineering, 22(6), 784–797. https://doi.org/10.1109/TKDE.2010.29
  • Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O., & Acharya, U. R. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, 121, 103792. https://doi.org/10.1016/j.compbiomed.2020.103792
  • Qin, L., Sun, Q., Wang, Y., Wu, K.-F., Chen, M., Shia, B.-C., & Wu, S.-Y. (2020). Prediction of number of cases of 2019 novel coronavirus (COVID-19) using social media search index. International Journal of Environmental Research and Public Health, 17(7), 2365. https://doi.org/10.3390/ijerph17072365
  • Schuster, M., & Nakajima, K. (2012). Japanese and korean voice search. In 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5149–5152). IEEE.
  • Shan, G., Zhou, L., & Zhang, D. (2021). From conflicts and confusion to doubts: Examining review inconsistency for fake review detection. Decision Support Systems, 144, 113513. https://doi.org/10.1016/j.dss.2021.113513
  • Shen, I., Zhang, L., Lian, J., Wu, C.-H., Fierro, M. G., Argyriou, A., & Wu, T. (2020). In search for a cure: Recommendation with knowledge graph on CORD-19. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3519–3520).
  • Su, H., Li, H., & Li, D. (2023). Knowledge reasoning with multiple relational paths. Connection Science, 1–21. https://doi.org/10.1080/09540091.2022.2161480
  • Suchanek, F. M., Kasneci, G., & Weikum, G. (2007). Yago: A core of semantic knowledge. In Proceedings of the 16th International Conference on World Wide Web (pp. 697–706).
  • Tandan, M., Acharya, Y., Pokharel, S., & Timilsina, M. (2021). Discovering symptom patterns of COVID-19 patients using association rule mining. Computers in Biology and Medicine, 131, 104249. https://doi.org/10.1016/j.compbiomed.2021.104249
  • Tomar, A., & Gupta, N. (2020). Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. Science of The Total Environment, 728, 138762. https://doi.org/10.1016/j.scitotenv.2020.138762
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
  • Wikipedia contributors. (2021). FAIR data – Wikipedia, The Free Encyclopedia. Online: Retrieved August 24, 2021, from https://en.wikipedia.org/w/index.php?title=FAIR_data&oldid=1038845392