Semantic alignment of ontologies meaningful categories with the generalization of descriptive structures
Abstract
The presented work addresses the issue of semantic alignment of ontology components with a generalized structured corpus. The field of research refers to the sphere of determining the features of trust in artificial intelligence. An alignment method is proposed at the level of semantic components of the general alignment system. The method is a component of a broader alignment system and compares entities at the level of meaningful correspondence. Moreover, only the alignment entities’ descriptive content is considered within the proposed technique. Descriptive contents can be represented by variously named id and semantic relations. The method defines a fundamental ontol- ogy and a specific alignment structure. Semantic correspondence in the form of information scope is formed from the alignment structure. In this way, an entity is formed on the side of the alignment structure, which would correspond in the best meaningful way to the entity from the ontology in terms of meaningful descriptiveness. Meaningful descriptiveness is the filling of information scope. Information scopes are formed as a final form of generalization and can consist of entities, a set of entities, and their partial union. In turn, entities are a generalization of properties that are located at a lower level of the hierarchy and, in turn, are a combination of descriptors. Descriptors are a fundamental element of generalization that represent principal content. Descriptors can define atomic content within a knowledge base and represent only a particular aspect of the content. Thus, the element of meaningfulness is not self-sufficient and can manifest as separate meaningfulness in the form of a property, as a minimal representation of the meaningfulness of an alignment. Descriptors can also supplement the content at the level of information frameworks, entities, and properties. The essence of the alignment in the form of information scope cannot be represented as a descriptor or their combination. It happens because the descriptive descriptor does not represent the content in the completed form of the correspondence unit. The minimum structure of representation of information scope is in the form of properties. This form of organization of establishing the correspondence of the semantic level of alignment allows you to structure and formalize the information content for areas with a complex form of semantic mapping. The hierarchical representation of the generalization not only allows simplifying the formalization of semantic alignment but also enables the formation of information entities with the possibility of discretization of content at the level of descriptors. In turn, descriptors can expand meaningfulness at an arbitrary level of the generalization hierarchy. This provides quantization of informational content and flexibility of the alignment system with discretization at the level of descriptors. The proposed method is used to formalize the semantic alignment of ontology entities and areas of structured representation of information.
Prombles in programming 2022; 3-4: 355-363
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Algergawy A., Cheatham M., Faria D., Ferrara A., Fundulaki I., Harrow I., Hertling S., Jiménez-Ruiz E., Karam N., Khiat A., Lambrix P., Li H., Montanelli S., Paulheim H., Pesquita C., Saveta T., Schmidt D., Shvaiko P., Splendiani A., Thiéblin E., Trojahn dos Santos C., Vatascinov J., Zamazal O., Zhou L. Results of the Ontology Alignment Evaluation Initiative 2018: 13th International Workshop on Ontology Matching co-located with the 17th ISWC (OM 2018), Monterey, United States , October 2018. pp.76-116.
Althobaiti A. F. S. Comparison of Ontology-Based Semantic-Similarity Measures in the Biomedical Text. Journal of Computer and Communi- cations. 2017. Vol. 05, No. 02. pp. 1-17. CrossRef
Annane A., Bellahsene Z. GBKOM: A generic framework for BK-based ontology matching. Journal of Web Semantics. 2020. Vol. 63. pp. 100563. CrossRef
Barmak O., Krak Y., Manziuk E. Characteristics for choice of models in the ansables classification. CEUR Workshop Proceedings. 2018. Vol. 2139. pp.171-179. CrossRef
Barmak O., Krak I., Manziuk E. Diversity as The Basis for Effective Clustering-Based Classification. CEUR-WS. 2020. Vol. 2711. pp. 53-67.
Barmak O., Manziuk E., Krak I. Using piecewise hyper linear classification in multidimensional feature space for text content: 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine , September 17, 2019. pp.119-123. CrossRef
Boiko J., Pyatin I., Eromenko O., Stepanov M. Method of the adaptive decoding of self-orthogonal codes in telecommunication. Indonesian Journal of Electrical Engineering and Computer Science. 2020. Vol. 19, No. 3. pp. 1287-1296. UCrossRef
Chen J., Jiménez-Ruiz E., Horrocks I., Antonyrajah D., Hadian A., Lee J. Augmenting Ontology Alignment by Semantic Embedding and Dis- tant Supervision: The Semantic Web, Cham , Springer International Publishing, 2021. pp.392-408. CrossRef
Chu S.-C., Xue X., Pan J.-S., Wu X. Optimizing Ontology Alignment in Vector Space. Journal of Internet Technology. 2020. Vol. 21, No. 1. pp. 15-22.
Drakopoulos G., Voutos Y., Mylonas P. Recent Advances On Ontology Similarity Metrics: A Survey: 2020 5th South-East Europe Design Automa- tion, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), September 2020. pp.1-7. CrossRef
Garanina N., Sidorova E., Kononenko I., Gorlatch S. Using Multiple Semantic Measures for Coreference Resolution in Ontology Population. International Journal of Computing. 2017. Vol. 16, No. 3. pp. 166-175. CrossRef
Hao J., Chen M., Yu W., Sun Y., Wang W. Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, New York, NY, USA , Association for Computing Machinery, July 25, 2019. pp.1709-1719. CrossRef
Holter O. M., Myklebust E. B., Chen J., Jimenez-Ruiz E. Embedding OWL ontologies with OWL2Vec. CEUR Workshop Proceedings. 2019. Vol. 2456. pp. 33-36.
Hrytsyk V., Nazarkevych M. Real-Time Sensing, Reasoning and Adaptation for Computer Vision Systems: Lecture Notes in Computational Intelligence and Decision Making, Cham , Springer International Publishing, 2022. pp.573-585. CrossRef
Ivanova T., Popov M. Ontology Evaluation and Multilingualism: Proceedings of the 21st International Conference on Computer Sys- tems and Technologies '20, New York, NY, USA, Association for Computing Machinery, June 19, 2020. pp.215-222. CrossRef
Jiménez-Ruiz E., Agibetov A., Chen J., Samwald M., Cross V. Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules. arXiv:2003.05370 [cs]. 2020.
Jobin A., Ienca M., Vayena E. The global landscape of AI ethics guidelines. Nature Machine Intelligence. 2019. Vol. 1, No. 9. Pp. 389-399. CrossRef
Kakad S., Dhage S. Ontology Construction from Cross Domain Customer Reviews using Expectation Maximization and Semantic Simi- larity: 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), March 2021. Pp.19-23.CrossRef
Krak I., Barmak O., Manziuk E. Approach to Piecewise-Linear Classification in a Multi-dimensional Space of Features Based on Plane Visualization. Computational Intelligence, 2022, 38(3), pp. 921-946. CrossRef
Krak, Y. V., Barmak, A. V., Baraban, E. M. Usage of NURBS-approximation for construction of spatial model of human face. Journal of Automation and Information Sciences, 2011. 43(2), 71-81. CrossRef
Kryvonos I. G., Krak I. V., Barmak O. V., Bagriy R. O. New tools of alternative communication for persons with verbal communication disorders. Cybernetics and Systems Analysis, 2016. 52(5), 665-673. CrossRef
Lastra-Díaz J. J., Goikoetxea J., Hadj Taieb M. A., Garcia-Serrano A., Ben Aouicha M., Agirre E., Sánchez D. A large reproducible bench- mark of ontology-based methods and word embeddings for word similarity. Information Systems. 2021. Vol. 96. pp. 101636. CrossRef
Manziuk E., Krak I., Barmak O., Mazurets O., Kuznetsov V., Pylypiak O. Structural alignment method of conceptual categories of ontology and formalized domain. CEUR-WS. 2021. Vol. 3003. pp. 11-22.
Manziuk E., Barmak O., Krak I., Mazurets O., Skrypnyk T. Formal Model of Trustworthy Artificial Intelligence Based on Standardization. CEUR-WS. 2021. Vol. 2853. pp. 190-197.
Manziuk E. A., Wójcik W., Barmak O. V., Krak I. V., Kulias A. I., Drabovska V. A., Puhach V. M., Sundetov S., Mussabekova A. Approach to creating an ensemble on a hierarchy of clusters using model decisions correlation. Przegląd Elektrotechniczny. 2020. Vol. 96, No. 9. pp. 108-113. CrossRef
Martsenyuk V., Bernas M., Klos-Witkowska A., Gancarczy T. On Parallel Processing of Machine Learning Based On Big Data and Voronoi Tessellation: CEUR Workshop Proceedings, 2021. pp.104-113.
Morozova O., Nicheporuk A., Tetskyi A., Tkachov V. Methods and technologies for ensuring cybersecurity of industrial and web-oriented systems and networks. Radioelectronic and computer systems. 2021. No. 4. pp. 145-156. CrossRef
Nicheporuk A., Savenko O., Nicheporuk A., Nicheporuk Y. An Android Malware Detection Method Based on CNN Mixed-data Model2020. pp.198-213.
Nkisi-Orji I., Wiratunga N., Massie S., Hui K.-Y., Heaven R. Ontology Alignment Based on Word Embedding and Random Forest Clas- sification: Machine Learning and Knowledge Discovery in Databases, Cham , Springer International Publishing, 2019. pp.557-572. CrossRef
Patel A., Jain S. A Partition Based Framework for Large Scale Ontology Matching. Recent Patents on Engineering. 2020. Vol. 14, No. 3. pp. 488-501. CrossRef
Portisch J. P. Towards Matching of Domain-Specific Schemas Using General-Purpose External Background Knowledge: The Semantic Web: ESWC 2020 Satellite Events, Cham , Springer International Publishing, 2020. pp.270-279.CrossRef
Qi Z., Zhang Z., Chen J., Chen X., Zheng Y. PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System. arXiv:2106.08801 [cs]. 2021. CrossRef
Rathee P., Malik S. K. IWD towards Semantic similarity measure in ontology. Journal of Information and Optimization Sciences. 2020. Vol. 41, No. 7. pp. 1561-1577.CrossRef
Rinaldi A. M., Russo C., Madani K. A Semantic Matching Strategy for Very Large Knowledge Bases Integration. International Journal of Information Technology and Web Engineering (IJITWE). 2020. Vol. 15, No. 2. pp. 1-29. CrossRef
Singh H., Jain P., Mausam, Chakrabarti S. Multilingual Knowledge Graph Completion with Joint Relation and Entity Alignment. arXiv:2104.08804 [cs]. 2021.
Sunilkumar P., Shaji A. P. A Survey on Semantic Similarity: 2019 International Conference on Advances in Computing, Communication and Control (ICAC3), December 2019. pp.1-8. CrossRef
Vedernikov M., Zelena M., Volianska-Savchuk L., Litinska V., Boiko J. Management of the Social Package Structure at Industrial Enter- prises on the Basis of Cluster Analysis. TEM Journal. 2020. Vol. 9, No. 1. pp. 249‐260. CrossRef
Xiang Y., Zhang Z., Chen J., Chen X., Lin Z., Zheng Y. OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding. arXiv:2105.07688 [cs]. 2021. CrossRef
Zeng K., Li C., Hou L., Li J., Feng L. A comprehensive survey of entity alignment for knowledge graphs. AI Open. 2021. Vol. 2. pp. 1-13. CrossRef
Zhao C., Wang Z. GOGO: An improved algorithm to measure the semantic similarity between gene ontology terms. Scientific Reports. 2018. Vol. 8, No. 1. pp. 1-10. CrossRef
Zhong B., Li H., Luo H., Zhou J., Fang W., Xing X. Ontology-Based Semantic Modeling of Knowledge in Construction: Classification and Identification of Hazards Implied in Images. Journal of Construction Engineering and Management. 2020. Vol. 146, No. 4. pp. 04020013. CrossRef
Zhu G., Iglesias C. A. Exploiting semantic similarity for named entity disambiguation in knowledge graphs. Expert Systems with Applications. 2018. Vol. 101. pp. 8-24. CrossRef
DOI: https://doi.org/10.15407/pp2022.03-04.355
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