Interactive complex ontology matching with local and global similarity deviations

Xingsi Xue; Miao Ye; Xingsi Xue; Miao Ye

doi:10.3934/era.2023291

Electronic Research Archive

2023, Volume 31, Issue 9: 5732-5748. doi: 10.3934/era.2023291

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Interactive complex ontology matching with local and global similarity deviations

Xingsi Xue ^{1,2
,
,},
Miao Ye ³

1.
Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China
2.
Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, Guilin Universitiy of Electronic Technology, Guilin 541004, China
3.
School of Information and Communication, Guilin University of Electronic Technology, Guilin 540014, China

Academic Editor: Yiu-ming Cheung

Received: 12 July 2023 Revised: 05 August 2023 Accepted: 13 August 2023 Published: 21 August 2023

Ontology serves as a central technique in the semantic web to elucidate domain knowledge. The challenge of dealing with the heterogeneity introduced by diverse domain ontologies necessitates ontology matching, a process designed to identify semantically interconnected entities within these ontologies. This task is inherently complex due to the broad, diverse entities and the rich semantics inherent in vocabularies. To tackle this challenge, we bring forth a new interactive ontology matching method with local and global similarity deviations (IOM-LGSD) for ontology matching, which consists of three novel components. First, a local and global similarity deviation (LGSD) metrics are presented to measure the consistency of similarity measures (SMs) and single out the less consistent SMs for user validation. Second, we present a genetic algorithm (GA) based SM selector to evolve the SM subsets. Lastly, a problem-specific induced ordered weighting aggregating (IOWA) operator based SM aggregator is proposed to assess the quality of selected SMs. The experiment evaluates IOM-LGSD with the ontology alignment evaluation initiative (OAEI) Benchmark and three real-world sensor ontologies. The evaluation underscores the effectiveness of IOM-LGSD in efficiently identifying high-quality ontology alignments, which consistently outperforms comparative methods in terms of effectiveness and efficiency.
- ontology matching,
- similarity measure,
- similarity deviation,
- induced ordered weighting aggregating,
- genetic algorithm
Citation: Xingsi Xue, Miao Ye. Interactive complex ontology matching with local and global similarity deviations[J]. Electronic Research Archive, 2023, 31(9): 5732-5748. doi: 10.3934/era.2023291

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Abstract

Ontology serves as a central technique in the semantic web to elucidate domain knowledge. The challenge of dealing with the heterogeneity introduced by diverse domain ontologies necessitates ontology matching, a process designed to identify semantically interconnected entities within these ontologies. This task is inherently complex due to the broad, diverse entities and the rich semantics inherent in vocabularies. To tackle this challenge, we bring forth a new interactive ontology matching method with local and global similarity deviations (IOM-LGSD) for ontology matching, which consists of three novel components. First, a local and global similarity deviation (LGSD) metrics are presented to measure the consistency of similarity measures (SMs) and single out the less consistent SMs for user validation. Second, we present a genetic algorithm (GA) based SM selector to evolve the SM subsets. Lastly, a problem-specific induced ordered weighting aggregating (IOWA) operator based SM aggregator is proposed to assess the quality of selected SMs. The experiment evaluates IOM-LGSD with the ontology alignment evaluation initiative (OAEI) Benchmark and three real-world sensor ontologies. The evaluation underscores the effectiveness of IOM-LGSD in efficiently identifying high-quality ontology alignments, which consistently outperforms comparative methods in terms of effectiveness and efficiency.

References

[1]	T. Berners-Lee, J. Hendler, O. Lassila, The semantic web, Sci. Am., 284 (2001), 34–43.
[2]	N. Guarino, D. Oberle, S. Staab, What is an ontology?, in Handbook on Ontologies, Springer, Berlin, 2009. https://doi.org/10.1007/978-3-540-92673-3_0
[3]	S. Das, P. Hussey, Hl7-fhir-based contsys formal ontology for enabling continuity of care data interoperability, J. Pers. Med., 13 (2023), 1024. https://doi.org/10.3390/jpm13071024 doi: 10.3390/jpm13071024
[4]	A. Sharma, S. Kumar, Machine learning and ontology-based novel semantic document indexing for information retrieval, Comput. Ind. Eng., 176 (2023), 108940. https://doi.org/10.1016/j.cie.2022.108940 doi: 10.1016/j.cie.2022.108940
[5]	M. A. Osman, S. A. M. Noah, S. Saad, Ontology-based knowledge management tools for knowledge sharing in organization—A review, IEEE Access, 10 (2022), 43267–43283. https://doi.org/10.1109/ACCESS.2022.3163758 doi: 10.1109/ACCESS.2022.3163758
[6]	S. K. Narayanasamy, K. Srinivasan, Y. C. Hu, S. K. Masilamani, K. Y. Huang, A contemporary review on utilizing semantic web technologies in healthcare, virtual communities, and ontology-based information processing systems, Electronics, 11 (2022), 453. https://doi.org/10.3390/electronics11030453 doi: 10.3390/electronics11030453
[7]	X. Zhou, Q. Lv, A. Geng, Matching heterogeneous ontologies based on multi-strategy adaptive co-firefly algorithm, Knowl. Inf. Syst., 65 (2023), 2619–2644. https://doi.org/10.1007/s10115-023-01845-2 doi: 10.1007/s10115-023-01845-2
[8]	J. Portisch, M. Hladik, H. Paulheim, Background knowledge in ontology matching: A survey, Semant. Web, 1–55. https://doi.org/10.3233/SW-223085
[9]	M. A. Khoudja, M. Fareh, H. Bouarfa, Deep embedding learning with auto-encoder for large-scale ontology matching, Int. J. Semant. Web Inf. Syst., 18 (2022), 1–18.
[10]	Y. Djenouri, H. Belhadi, K. Akli-Astouati, A. Cano, J. C. W. Lin, An ontology matching approach for semantic modeling: A case study in smart cities, Comput. Intell., 38 (2022), 876–902. https://doi.org/10.1111/coin.12474 doi: 10.1111/coin.12474
[11]	X. Kou, J. Feng, Y. Wang, W. Cui, A multi-objective particle swarm optimization for matching domain ontologies, Int. Technol. Lett., e405. https://doi.org/10.1002/itl2.405
[12]	S. Ibrahim, S. Fathalla, J. Lehmann, H. Jabeen, Toward the multilingual semantic web: Multilingual ontology matching and assessment, IEEE Access, 11 (2023), 8581–8599. https://doi.org/10.1109/ACCESS.2023.3238871 doi: 10.1109/ACCESS.2023.3238871
[13]	T. Y. Wu, A. Shao, J. S. Pan, Ctoa: Toward a chaotic-based tumbleweed optimization algorithm, Mathematics, 11 (2023), 2339. https://doi.org/10.3390/math11102339 doi: 10.3390/math11102339
[14]	S. Forrest, Genetic algorithms, ACM Comput. Surv., 28 (1996), 77–80.
[15]	N. Krishnan, G. Deepak, Easdisco: Toward a novel framework for web service discovery using ontology matching and genetic algorithm, in Advances in Data Computing, Communication and Security: Proceedings of I3CS2021, Springer, (2022), 283–291.
[16]	X. Xue, J. Chen, Matching biomedical ontologies through compact differential evolution algorithm with compact adaption schemes on control parameters, Neurocomputing, 458 (2021), 526–534. https://doi.org/10.1016/j.neucom.2020.03.122 doi: 10.1016/j.neucom.2020.03.122
[17]	H. Li, Z. Dragisic, D. Faria, V. Ivanova, E. Jiménez-Ruiz, P. Lambrix, et al., User validation in ontology alignment: functional assessment and impact, Knowl. Eng. Rev., 34 (2019), e15. https://doi.org/10.1017/S0269888919000080 doi: 10.1017/S0269888919000080
[18]	T. Y. Wu, H. Li, S. C. Chu, Cppe: An improved phasmatodea population evolution algorithm with chaotic maps, Mathematics, 11 (2023), 1977. https://doi.org/10.3390/math11091977 doi: 10.3390/math11091977
[19]	A. Dadgar, Y. Baleghi, M. Ezoji, Multi-view data fusion in multi-object tracking with probability density-based ordered weighted aggregation, Optik, 262 (2022), 169279. https://doi.org/10.1016/j.ijleo.2022.169279 doi: 10.1016/j.ijleo.2022.169279
[20]	X. Xue, J. Guo, M. Ye, J. Lv, Similarity feature construction for matching ontologies through adaptively aggregating artificial neural networks, Mathematics, 11 (2023), 485. https://doi.org/10.3390/math11020485 doi: 10.3390/math11020485
[21]	B. Smith, Ontology, in The Furniture of the World, Brill, (2012), 47–68. https://doi.org/10.1163/9789401207799_005
[22]	P. Shvaiko, J. Euzenat, Ontology matching: state of the art and future challenges, IEEE Trans. Knowl. Data Eng., 25 (2011), 158–176. https://doi.org/10.1109/TKDE.2011.253 doi: 10.1109/TKDE.2011.253
[23]	V. M. Tayur, R. Suchithra, Multi-ontology mapping generative adversarial network in internet of things for ontology alignment, Int. Things, 20 (2022), 100616. https://doi.org/10.1016/j.iot.2022.100616 doi: 10.1016/j.iot.2022.100616
[24]	C. Trojahn, R. Vieira, D. Schmidt, A. Pease, G. Guizzardi, Foundational ontologies meet ontology matching: A survey, Semant. Web, 13 (2022), 685–704. https://doi.org/10.3233/SW-210447 doi: 10.3233/SW-210447
[25]	X. Xue, Q. Huang, Generative adversarial learning for optimizing ontology alignment, Expert Syst., 40 (2023), e12936. https://doi.org/10.1111/exsy.12936 doi: 10.1111/exsy.12936
[26]	J. Fürst, M. Fadel Argerich, B. Cheng, Versamatch: ontology matching with weak supervision, in 49th Conference on Very Large Data Bases (VLDB), 16 (2023), 1305–1318.
[27]	X. Xue, Y. Wang, W. Hao, Using moea/d for optimizing ontology alignments, Soft Comput., 18 (2014), 1589–1601. https://doi.org/10.1007/s00500-013-1165-9 doi: 10.1007/s00500-013-1165-9
[28]	J. Berlin, A. Motro, Database schema matching using machine learning with feature selection, in Advanced Information Systems Engineering, Springer, (2002), 452–466. https://doi.org/10.1007/3-540-47961-9_32
[29]	C. C. Kiu, C. S. Lee, Ontodna: Ontology alignment results for oaei 2007, in Proceedings of the 2nd International Workshop on Ontology Matching (OM-2007) Collocated with the 6th International Semantic Web Conference (ISWC-2007) and the 2nd Asian Semantic Web Conference (ASWC-2007), (2007), 304.
[30]	E. Jiménez-Ruiz, B. C. Grau, Y. Zhou, Logmap 2.0: towards logic-based, scalable and interactive ontology matching, in Proceedings of the 4th international workshop on semantic web applications and tools for the life sciences, (2011), 45–46. https://doi.org/10.1145/2166896.2166911
[31]	U. Thayasivam, P. Doshi, Optima results for OAEI 2011, in Proc. of 6th OM Workshop, Citeseer, (2011), 204–211.
[32]	S. Hertling, Hertuda results for OEAI 2012, Ontology Matching, 141.
[33]	M. C. Silva, D. Faria, C. Pesquita, Extending agreementmakerlight to perform holistic ontology matching, in The Semantic Web: ESWC 2022 Satellite Events, Springer, (2022), 31–35. https://doi.org/10.1007/978-3-031-11609-4_6
[34]	K. Janani, S. Mohanrasu, C. P. Lim, B. Manavalan, R. Rakkiyappan, Ensemble feature selection using Bonferroni, OWA and induced OWA aggregation operators, Appl. Soft Comput., 143 (2023), 110431. https://doi.org/10.1016/j.asoc.2023.110431 doi: 10.1016/j.asoc.2023.110431
[35]	O. Moroz, V. Stepashko, New two-parametric mutation operator for inductive modelling using combinatorial-genetic algorithm, in 2022 12th International Conference on Advanced Computer Information Technologies, IEEE, (2022), 76–79. https://doi.org/10.1109/ACIT54803.2022.9913199
[36]	F. Giunchiglia, A. Autayeu, J. Pane, S-match: An open source framework for matching lightweight ontologies, Semant. Web, 3 (2012), 307–317. https://doi.org/10.3233/SW-2011-0036 doi: 10.3233/SW-2011-0036
[37]	J. R. Gomes, A. L. Gançarski, P. R. Henriques, Omt, a web-based tool for ontology matching, in 11th Symposium on Languages, Applications and Technologies (SLATE 2022), 2022. https://doi.org/10.4230/OASIcs.SLATE.2022.8
[38]	X. Liu, Q. Tong, X. Liu, Z. Qin, Ontology matching: State of the art, future challenges, and thinking based on utilized information, IEEE Access, 9 (2021), 91235–91243. https://doi.org/10.1109/ACCESS.2021.3057081 doi: 10.1109/ACCESS.2021.3057081
[39]	N. Mahmoud, H. M. Abdlkader, Enhanced ontology matching for big data integration, J. Phys.: Conf. Ser., 1447 (2020), 012028. https://doi.org/10.1088/1742-6596/1447/1/012028 doi: 10.1088/1742-6596/1447/1/012028

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