Identifying Terms to Represent Concepts of a Work Process Ontology

Mohamad Amin Osman; Shahrul Azman Mohd Noah  lowast; Saidah Saad

doi:10.17485/IJST/v17i24.1597

Article

Identifying Terms to Represent Concepts of a Work Process Ontology

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i24.1597

Year: 2024, Volume: 17, Issue: 24, Pages: 2519-2528

Original Article

Identifying Terms to Represent Concepts of a Work Process Ontology

Mohamad Amin Osman¹, Shahrul Azman Mohd Noah^1∗, Saidah Saad¹

¹Center for Artificial Intelligence Technology, Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Malaysia

*Corresponding Author
Email: [email protected]

Received Date:27 June 2023, Accepted Date:01 April 2024, Published Date:13 June 2024

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

Abstract

Objectives: To identify ontology concepts from text documents for the construction of work process ontology. Methods: This study proposes a methodology to identify terms representing a work process ontology concept from a document. The methodology encompasses several key steps: document collecting, text pre-processing, term weighting and analysis, terms mapping, and domain expert relevance judgments. A comparison between the results of three different term weighting schemes, namely the Term Frequency (TF), Term Frequency-Inverse Document Frequency (TFIDF), and Mutual Information (MI) is made with the ontology concept that the domain expert has judged. Findings: The approaches adopted in this study have managed to extract ontological concepts from the targeted domain knowledge source. The findings of the comparison analysis suggest that the TFIDF term weighting scheme exhibits better results compared to the TF and MI weighting schemes. Novelty: A work process ontology is a structured knowledge describing daily operations in the government sector. However, there has been little to no effort in building the work process ontology. This study presents an integrated approach for identifying ontology concepts from documents within the domain of the work process. To the utmost extent of our understanding, this research initiative is the initial attempt to introduce a structured methodology for the semi-automatic extraction and evaluation of concepts and relationships within this domain. The findings can be utilised as a foundation for developing an ontology in the specific field.

Keywords: Ontology, Work process, Text extraction, Natural language processing, Term weighting

References

Konopka T, Smedley D. A pan-ontology view of machine-derived knowledge representations and feedback mechanisms for curation. bioRxiv preprint. 2021;p. 1–29. Available from: https://doi.org/10.1101/2021.03.02.433532
Scarpato N, Cilia ND, Romano M. Reachability Matrix Ontology: A Cybersecurity Ontology. Applied Artificial Intelligence. 2019;33(7):643–655. Available from: https://dx.doi.org/10.1080/08839514.2019.1592344
Garcia LF, Abel M, Perrin M, Alvarenga RdS. The GeoCore ontology: A core ontology for general use in Geology. Computers & Geosciences. 2020;135. Available from: https://dx.doi.org/10.1016/j.cageo.2019.104387
Calvo-Cidoncha E, Camacho-Hernando C, Feu F, Pastor-Duran X, Codina-Jané C, Lozano-Rubí R. OntoPharma: ontology based clinical decision support system to reduce medication prescribing errors. BMC Medical Informatics and Decision Making. 2022;22(1):1–12. Available from: https://dx.doi.org/10.1186/s12911-022-01979-3
Shukla D, Azad HK, Abhishek K, Shitharth S. Disaster management ontology- an ontological approach to disaster management automation. Scientific Reports. 2023;13(1):1–15. Available from: https://doi.org/10.1038/s41598-023-34874-6
Mahria BB, Chaker I, Zahi A. A novel approach for learning ontology from relational database: from the construction to the evaluation. Journal of Big Data. 2021;8(1):1–22. Available from: https://dx.doi.org/10.1186/s40537-021-00412-2
Yun W, Zhang X, Li Z, Liu H, Han M. Knowledge modeling: A survey of processes and techniques. International Journal of Intelligent Systems. 2021;36(4):1686–1720. Available from: https://dx.doi.org/10.1002/int.22357
Zulkipli ZZ, Maskat R, Teo NHI. A systematic literature review of automatic ontology construction. Indonesian Journal of Electrical Engineering and Computer Science. 2022;28(2):878–889. Available from: https://dx.doi.org/10.11591/ijeecs.v28.i2.pp878-889
Guimarães NC, Carvalho CLD. A modular framework for ontology learning from text in Portuguese. Multi-Science Journal. 2020;3(3):37–42. Available from: https://dx.doi.org/10.33837/msj.v3i3.899
Babič F, Bureš V, Čech P, Husáková M, Mikulecký P, Mls K, et al. Review of Tools for Semantics Extraction: Application in Tsunami Research Domain. Information. 2022;13(1):1–30. Available from: https://dx.doi.org/10.3390/info13010004
Watróbski J. Ontology learning methods from text - an extensive knowledge-based approach. Procedia Computer Science. 2020;176:3356–3368. Available from: https://doi.org/10.1016/j.procs.2020.09.061
Saeed MY, Awais M, Younas M, Shah MA, Khan A, Uddin MI, et al. An abstractive summarisation technique with variable length keywords as per document diversity. Computers, Materials & Continua. 2021;66(3):2409–2423. Available from: https://doi.org/10.32604/cmc.2021.014330
Surana S, Pathak K, Gagnani M, Shrivastava V, Madhuri GS. Text Extraction and Detection from Images using Machine Learning Techniques: A Research Review. In: 2022 International Conference on Electronics and Renewable Systems (ICEARS). (pp. 1201-1207) IEEE. 2022.
Zhang B, Wang Q. Outfit Helper: A Dialogue-Based System for Solving the Problem of Outfit Matching. Journal of Computer and Communications. 2019;7(12):50–65. Available from: https://dx.doi.org/10.4236/jcc.2019.712006
Gomez MJ, Ruiperez-Valiente JA, Clemente FJG. Analyzing Trends and Patterns Across the Educational Technology Communities Using Fontana Framework. IEEE Access. 2022;10:35336–35351. Available from: https://dx.doi.org/10.1109/access.2022.3163253
Javed MA, Muram FU, Kanwal S. Ontology-Based Natural Language Processing for Process Compliance Management. In: International Conference on Evaluation of Novel Approaches to Software Engineering, Communications in Computer and Information Science. (Vol. 1556, pp. 309-327) Springer, Cham. 2022.
Fawei B, Pan JZ, Kollingbaum M, Wyner AZ. A Semi-automated Ontology Construction for Legal Question Answering. New Generation Computing. 2019;37(4):453–478. Available from: https://dx.doi.org/10.1007/s00354-019-00070-2
Ayadi A, Samet A, Beuvron FdBd, Zanni-Merk C. Ontology population with deep learning-based NLP: a case study on the Biomolecular Network Ontology. Procedia Computer Science. 2019;159:572–581. Available from: https://dx.doi.org/10.1016/j.procs.2019.09.212
Alemán Y, Somodevilla MJ, Vilariño D. Semi-Automatic Creation of Ontologies from Unstructured Pedagogical Texts to Assist in Significant Learning. Computación y Sistemas. 2022;26(1):245–260. Available from: https://dx.doi.org/10.13053/cys-26-1-4168
Gómez-Suta M, Echeverry-Correa JD, Soto-Mejía JA. Semi-automatic extraction and validation of concepts in ontology learning from texts in Spanish. In: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics. (Vol. 1625, pp. 7-16) New York, NY, United States. Association for Computing Machinery. 2020.
Korel L, Yorsh U, Behr AS, Kockmann N, Holeňa M. Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis. Computers. 2023;12(1):1–25. Available from: https://dx.doi.org/10.3390/computers12010014
Alexander NS, Yusof A. A Study of Ontology Engineering in Malay Unstructured Document Using Entity Relationship Model. Journal of Emerging Technologies and Industrial Applications. 2022;1(2):1–8. Available from: http://jetia.mbot.org.my/index.php/jetia/article/view/17
Fu Y, Lin N, Yang Z, Jiang S. An Open-Source Dataset and A Multi-Task Model for Malay Named Entity Recognition. arXiv. 2021;p. 1–12. Available from: https://doi.org/10.48550/arXiv.2109.01293
Awatramani P, Daware R, Chouhan H, Vaswani A, Khedkar S. Sentiment Analysis of Mixed-Case Language using Natural Language Processing. In: 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). (pp. 651-658) IEEE. 2021.
Husein Z. Natural-Language-Toolkit library for Bahasa Malaysia. github. Available from: https://github.com/huseinzol05/malaya
Gui X, Deng L, Jiang G. The Use of Corpus in English Writing: Scholarly Development and Implications. Journal of Education and Educational Research. 2022;1(1):64–71. Available from: https://dx.doi.org/10.54097/jeer.v1i1.2476
Chiang TA, Che ZH, Huang YL, Tsai CY. Using an Ontology-Based Neural Network and DEA to Discover Deficiencies of Hotel Services. International Journal on Semantic Web and Information Systems. 2022;18(1):1–19. Available from: https://dx.doi.org/10.4018/ijswis.306748
HaCohen-Kerner Y, Miller D, Yigal Y. The influence of preprocessing on text classification using a bag-of-words representation. PLOS ONE. 2020;15(5):1–22. Available from: https://dx.doi.org/10.1371/journal.pone.0232525
Sarode V, Joshi B, Savakare T, Warule H. A Real Time Chatbot Using Python. International Journal for Research in Applied Science and Engineering Technology. 2023;11(5):7385–7389. Available from: https://dx.doi.org/10.22214/ijraset.2023.53453
Hickman L, Thapa S, Tay L, Cao M, Srinivasan P. Text Preprocessing for Text Mining in Organizational Research: Review and Recommendations. Organizational Research Methods. 2022;25(1):114–146. Available from: https://doi.org/10.1177/1094428120971683
Guo B, Zhang C, Liu J, Ma X. Improving text classification with weighted word embeddings via a multi-channel TextCNN model. Neurocomputing. 2019;363:366–374. Available from: https://dx.doi.org/10.1016/j.neucom.2019.07.052
Albalawi R, Yeap TH, Benyoucef M. Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis. Frontiers in Artificial Intelligence. 2020;3:1–14. Available from: https://dx.doi.org/10.3389/frai.2020.00042
Yuan H, Tang Y, Sun W, Liu L. A detection method for android application security based on TF-IDF and machine learning. PLOS ONE. 2020;15(9):1–19. Available from: https://dx.doi.org/10.1371/journal.pone.0238694
Patil R, Boit S, Gudivada V, Nandigam J. A Survey of Text Representation and Embedding Techniques in NLP. IEEE Access. 2023;11:36120–36146. Available from: https://dx.doi.org/10.1109/access.2023.3266377
Sulistiani H, Muludi K, Syarif A. Implementation of Dynamic Mutual Information and Support Vector Machine for Customer Loyalty Classification. In: The 2nd International Conference on Applied Sciences Mathematics and Informatics , Journal of Physics: Conference Series. (Vol. 1338, pp. 1-8) IOP Publishing. 2019.
Liu Y, Shi M, Li C. Domain ontology concept extraction method based on text. In: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). IEEE. 2016.
Nguyen TMH, Webb S. Examining second language receptive knowledge of collocation and factors that affect learning. Language Teaching Research. 2017;21(3):298–320. Available from: https://dx.doi.org/10.1177/1362168816639619
Prasetiyowati MI, Maulidevi NU, Surendro K. Determining threshold value on information gain feature selection to increase speed and prediction accuracy of random forest. Journal of Big Data. 2021;8(1):1–22. Available from: https://dx.doi.org/10.1186/s40537-021-00472-4
Ramli F, Noah SAM, Kurniawan TB. Using Ontology-Based Approach to Improved Information Retrieval Semantically for Historical Domain. International Journal on Advanced Science, Engineering and Information Technology. 2020;10(3):1130–1136. Available from: https://doi.org/10.18517/ijaseit.10.3.10180
Strömert P, Hunold J, Castro A, Neumann S, Koepler O. Ontologies4Chem: the landscape of ontologies in chemistry. Pure and Applied Chemistry. 2022;94(6):605–622. Available from: https://doi.org/10.1515/pac-2021-2007

Copyright

© 2024 Osman et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)