Research of modern methods and tools of ontological engineering in the context of creating intellectual systems
DOI:
https://doi.org/10.30837/2522-9818.2025.1.032Keywords:
ontology engineering; semantic modeling; knowledge bases; ontology languages; ontology tools.Abstract
The subject of the work is the study of modern methods and tools of ontological engineering in the context of developing intelligent systems. In particular, ontological engineering as a process of creating formalized knowledge bases for intelligent systems. The work analyzes ontological modeling languages (RDF, OWL, SWRL, etc.), the SPARQL query language and tools such as Protégé, Hozo, etc., which allow implementing the specified approach. The purpose of the research is to identify and analyze modern tools and methods of ontological engineering for developing knowledge bases that provide high prediction accuracy and adaptability in complex intelligent systems using artificial intelligence or other prediction algorithms. The objective of this study is to conduct a comprehensive analysis of modern methods and tools of ontology engineering, including a comparative description of their capabilities and limitations when applied in intelligent systems. The study involves studying methods for automating the processes of creating and updating ontologies, in particular using deep learning and natural language processing, as well as evaluating promising languages and tools for modeling ontology. Particular attention is paid to the analysis of the application of ontologies in various subject areas where high forecasting accuracy is required, with further research into methods for optimizing queries to ontological databases. The practical part of the work involves creating a demonstration ontological knowledge base for an intelligent control system for virtual distributed power plants, which will allow assessing the practical value of the study. The research methods include a thorough analysis of the literature and available documentation on the topic, a comparative analysis of the obtained data and further demonstration in practice of the application of selected tools and methods, based on their indicators. Results The issue of the feasibility of ontological engineering in modern intelligent systems is considered. Tools and methods for creating knowledge bases based on ontologies are presented and analyzed. The paper provides comparative statistics of modern tools for creating ontologies. A substantiated vision of the situation around the tools and methods of ontological engineering was put forward. The limitations and unique aspects of each of the considered tools are revealed. Optimal approaches to creating knowledge bases for intelligent systems are determined. The use and features of ontological engineering tools are demonstrated on a practical example in the context of intelligent systems for controlling virtual distributed power plants. In conclusion, the novelty of the study lies in the modernity of the view on the issue of choosing tools for creating knowledge bases using ontological engineering, in particular in terms of their use in intelligent systems, in combination with artificial intelligence.The results of this work can be used in the development of relevant intelligent systems that use knowledge bases. The study provides a wide range of information and comparisons of ontological engineering approaches and tools.
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Список літератури
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References
Pouchard, L., Ivezic, N., Schlenoff, C. "Ontology engineering for distributed collaboration in manufacturing. AIS2000 Conf". 2000. 1012 р. available at: https://www.researchgate.net/publication/228596365_Ontology_engineering_for_distributed_collaboration_in_manufacturing (last accessed: 02.12.2024).
Guarino, N. "Ontologies and knowledge bases: towards a terminological clarification, Towards Very Large Knowl. Bases. Amsterdam:IOS Press". 1995. P. 25–32. available at: https://www.researchgate.net/publication/220041941_Ontologies_and_knowledge_bases_towards_a_terminological_clarification (last accessed: 02.12.2024).
Motz, R., Rohrer, E., Severi, P. (2015), "The description logicSHIQwith a flexible meta-modelling hierarchy". Journal of Web Semantics. 2015. Vol. 35. P. 214–234. DOI: 10.1016/j.websem.2015.05.002
"jsld.org. jsld.org". available at: https://jsld.org/ (last accessed: 02.12.2024).
Kellogg, G., Champin, P.A., Longley, D. "JSON-LD 1.1 – A JSON-based Serialization for Linked Data". 2020. available at: https://hal.science/hal-02141614v2 (last accessed: 02.12.2024).
"ISO/IEC 24707:2018. ISO". available at: https://www.iso.org/standard/66249.html (last accessed: 02.12.2024).
"GitHub – gruninger/colore: Automatically exported from code.google.com/p/colore. GitHub". available at: https://github.com/gruninger/colore (last accessed: 22.11.2024).
"Hets (The heterogeneous tool set). Hets (The heterogeneous tool set) ". available at: http://hets.eu/ (last accessed: 22.11.2024).
"GitHub – cmungall/cltools: tools for common-logic. GitHub". available at: https://github.com/cmungall/cltools (last accessed: 22.11.2024).
"Home – cyc. Cyc – The Next Generation of Enterprise AI". available at: https://cyc.com/ (last accessed: 22.11.2024).
Renssen, V., "Ashp. Gellish – A generic extensible ontological language – design and application of
a universal data structure. Delft: Delft University Press". 2005. 238 р. available at: https://www.researchgate.net/publication/339529625_Gellish_A_Generic_Extensible_Ontological_Language_-_Design_and_Application_of_a_Universal_Data_Structure
Maniraj, V., Sivakumar, D. "Ontology Languages – A Review. International Journal of Computer Theory and Engineering". 2010. Vol. 6, no. 2. P. 887. available at: https://www.researchgate.net/publication/269801838_Ontology_Languages_-_A_Review (last accessed: 22.11.2024).
Shen, W. "Multi-Agent systems for concurrent intelligent design and manufacturing. Taylor & Francis Group", 2019. 416 р.
"OWL: A Large Language Model for IT Operations / H. Guo et al. Computation and Language". 2023. available at: https://doi.org/10.48550/arXiv.2309.09298 (last accessed: 22.11.2024).
Koen de, J. "Transformation from OntoUML models to the OpenAPI Specification". 2024. available at: https://purl.utwente.nl/essays/98294 (last accessed: 22.11.2024).
Pareti, P. (2021), "A Review of SHACL: From Data Validation to Schema Reasoning for RDF Graphs". Reasoning Web. Declarative Artificial Intelligence. 2021. P. 115–144. DOI: 10.1007/978-3-030-95481-9_6
Hoitash, R., Hoitash, U., Morris, L. (2021), "eXtensible Business Reporting Language (XBRL): A Review and Implications for Future Research". AUDITING: A Journal of Practice & Theory. 2021. Vol. 40, No. 2. P. 107–132. DOI: 10.2308/ajpt-2019-517
"A survey of RDF stores & SPARQL engines for querying knowledge graphs / W. Ali et al. The VLDB Journal". 2021. No. 31. P. 1–26. available at: https://link.springer.com/article/10.1007/s00778-021-00711-3 (last accessed: 22.11.2024)
"Hozo – Ontology Editor. Hozo – Ontology Editor". available at: https://www.hozo.jp/ (last accessed: 22.11.2024).
"GraphRAG for enterprise GenAI – Lettria. GraphRAG for enterprise GenAI - Lettria". available at: https://www.lettria.com/ (last accessed: 22.11.2024).
"Protégé. protégé". available at: https://protege.stanford.edu/ (last accessed: 22.11.2024).
"Onto4ALL – Ontology Graphical Editor. Onto4ALL – Ontology Graphical Editor". available at: https://onto4all.com (last accessed: 22.11.2024).
"FluentEditor – Ontology Editor Semantic Web. Cognitum Software House". available at: https://www.cognitum.eu/semantics/fluenteditor/ (last accessed: 22.11.2024).
Pertsas, V., Constantopoulos, P. (2023), "Ontology-Driven Extraction of Contextualized Information from Research Publications". 15th International Conference on Knowledge Engineering and Ontology Development, Rome, Italy, 13–15 November 2023. DOI: 10.5220/0012254100003598
Salim, M. N., Mustafa, B. S. (2021), "UTtoKB: a Model for Semantic Relation Extraction from Unstructured Text". 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, 21–23 October 2021. DOI: 10.1109/ismsit52890.2021.9604538
Demelo, J., Sedig, K. (2021), "Forming Cognitive Maps of Ontologies Using Interactive Visualizations". Multimodal Technologies and Interaction. 2021. Vol. 5, No. 1. 2 р. DOI: 10.3390/mti5010002
Dooley, D., Nguyen, M. H., Hsiao, W. W. L. (2023), "OntoTrek: 3D visualization of application ontology class hierarchies". PLOS ONE. 2023. Vol. 18, No. 6. DOI: 10.1371/journal.pone.0286728
Brahmia, Z., Grandi, F., Bouaziz, R. (2023), "τSQWRL: A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data". Big Data Mining and Analytics. 2023. Vol. 6, No. 3. P. 288–300. DOI: 10.26599/bdma.2022.9020044
Bolatito, Y. A. (2024), "An Enhanced OWL-Time Ontology for Complex Recurring Temporal Patterns" / et al. Kasu Journal of Computer Science. 2024. Vol. 1, No. 2. P. 340–365. DOI: 10.47514/kjcs/2024.1.2.0013
Achich, N. (2020), "Approach to Reasoning about Uncertain Temporal Data in OWL 2", Procedia Computer Science. 2020. Vol. 176. P. 1141–1150. DOI:10.1016/j.procs.2020.09.110
Shanmurthy, P. (2022), "Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability", Indonesian Journal of Electrical Engineering and Computer Science. 2022. Vol. 27, No. 1. 504 р. DOI: 10.11591/ijeecs.v27.i1.pp504-512
Ertuğrul, D. Ç. (2023), "A knowledge-based decision support system for inferring supportive treatment recommendations for diabetes mellitus", Technology and Health Care. 2023. P. 1–24. DOI: 10.3233/thc-230237
Saha, R. (2021), "Ontology-based intelligent decision support systems: A systematic approach". Web Semantics. 2021. P. 177–193. DOI: 10.1016/b978-0-12-822468-7.00005-5`
Spoladore, D., Pessot, E. (2021), "Collaborative Ontology Engineering Methodologies for the Development of Decision Support Systems: Case Studies in the Healthcare Domain". Electronics. 2021. Vol. 10, No. 9. 1060 р. DOI:10.3390/electronics10091060
Teixeira, B. (2020), "Application Ontology for Multi-Agent and Web-Services’ Co-Simulation in Power and Energy Systems", IEEE Access. 2020. Vol. 8. P. 81129–81141. DOI: 10.1109/access.2020.2991010
Nachet, B., Frendi, M., Adla, A. (2024), "Physical Internet Enabled Traceability Systems for Sustainable Supply Chain Management". Journal of information and organizational sciences. 2024. Vol. 48, No. 1. P. 99–116. DOI: 10.31341/jios.48.1.5
Aslam, S., Vassilev, V. T., Ouazzane, K. (2019), "Parallel Querying of Distributed Ontologies with Shared Vocabulary". World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering. 2019. Vol. 13, No. 5. P. 287–294. DOI: 10.5281/zenodo.3298789
"Query Optimization for Large Scale Clustered RDF Data / I. Zouaghi et al". International Workshop on Data Warehousing and OLAP. 2020. P. 56–65. available at: https://api.semanticscholar.org/CorpusID:212727545 (last accessed: 05.02.2025).
Lin, X., Jiang, D. (2022), "A Two-Phase Method for Optimization of the SPARQL Query. Journal of Sensors. 2022. Vol. 2022. P. 1–12. DOI: 10.1155/2022/4624856
Kang, X. (2021), "Grace: An Efficient Parallel SPARQL Query System over Large-Scale RDF Data", 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Dalian, China, 5 – 7 May 2021. DOI: 10.1109/cscwd49262.2021.9437674
Agbaegbu, J. (2021), "Ontologies in Cloud Computing – Review and Future Directions", Future Internet. 2021. Vol. 13, No. 12. 302 р. DOI: 10.3390/fi13120302
Karataiev, O., Shubin, I. (2023), "Formal model of multi-agent architecture of a software system based on knowledge interpretation". Radioelectronic and Computer Systems. 2023. No. 4. P. 53–64. DOI: 10.32620/reks.2023.4.05
Xie, Y. (2024), "Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications". IEEE/CAA Journal of Automatica Sinica. 2024. Vol. 11, No. 2. P. 329–343. DOI: 10.1109/jas.2024.124218
Ullah, Z., Arshad, A., Nekahi, A. (2024), "Virtual Power Plants: Challenges, Opportunities, and Profitability Assessment in Current Energy Markets". Electricity. 2024. Vol. 5, No. 2. P. 370–384. DOI: 10.3390/electricity5020019
Zhang, W. (2022), "Virtual power plant integration with smart grids: a Review", 2022 IEEE 5th International Electrical and Energy Conference (CIEEC), Nangjing, China, 27–29 May 2022. DOI: 10.1109/cieec54735.2022.9846220
Bassiliades, N. (2020), "A Tool for Transforming Semantic Web Rule Language to SPARQL Infererecing Notation". International Journal on Semantic Web and Information Systems. 2020. Vol. 16, No. 1. P. 87–115. DOI: 10.4018/ijswis.2020010105
Calvanese, D. (2016), "Ontop: Answering SPARQL queries over relational databases", Semantic Web. 2016. Vol. 8, No. 3. P. 471–487. DOI.org/10.3233/sw-160217
Jajaga, E., Ahmedi, L. (2017), "C-SWRL: SWRL for Reasoning over Stream Data". 2017 IEEE 11th International Conference on Semantic Computing (ICSC), San Diego, CA, USA, 30 January – 1 February 2017. DOI: 10.1109/icsc.2017.64
Dudar, Z., Litvin, S. (2024), "Ontological description method for building service-oriented distributed learning systems". Innovative technologies and scientific solutions for industries. 2024. No. 1 (27). P. 39–53. DOI: 10.30837/itssi.2024.27.039
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