Abstract
In recent years, ontology integration has received an increased focus in ontology engineering. Ontology integration is a complex process that has some difficulties such as semantic heterogeneity. The goal of this research is to use semantic mapping to reduce integration complexity and solve semantic heterogeneity. What is ontology engineering? What difficulties haven't been solved until now by ontology integration? What is the effective role of semantic mapping in semantic heterogeneity? This research seeks to address these questions. The expected contribution of this research is to build a comprehensive view of ontology integration and support interoperability. The significance of using semantic mapping to improve interoperability on ontology integration is confirmed by researchers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ren G, Ding R, Li H (2019) Building an ontological knowledgebase for bridge maintenance. Adv Eng Softw 130:24–40
Huang X, Zanni-Merk C, Crémilleux B (2019) Enhancing Deep Learning with Semantics: an application to manufacturing time series analysis. Proc Comput Sci 159(2018):437–446
Zhang J, Li H, Zhao Y, Ren G (2018) An ontology-based approach supporting holistic structural design with the consideration of safety, environmental impact and cost. Adv Eng Softw 115:26–39
Shang Z, Wang M, Su D (2018) Ontology based social life cycle assessment for product development. Adv Mech Eng 10(11):1–17
Karray MH, Ameri F, Hodkiewicz M, Louge T (2019) ROMAIN: towards a BFO compliant reference ontology for industrial maintenance. Appl Ontol 14(2):155–177
Otte JN, Kiritsi D, Ali MM, Yang R, Zhang B, Rudnicki R, Rai R, Smith B (2019) An ontological approach to representing the product life cycle. Appl Ontol 14(2):179–197
Slimani T (2014) A study on ontologies and their classification. Recent Adv Electr Eng Educ Technol 2014:86–92
Mohammed M, Romli A, Mohamed R (2021) Existing semantic ontology and its challenges for enhancing interoperability in IoT environment. In: 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM). IEEE, pp. 22–26
Hobbs J, Fenn T (2019) The design of socially sustainable ontologies. Philos Technol 32(4):745–767
Mohd M, Bilo M, Louge T, Rai R, Hedi M (2020) Computers in industry ontology-based approach to extract product’s design features from online customers’ reviews. Comput Ind 116:103175
Cheng H, Zeng P, Xue L, Shi Z, Wang P, Yu H (2016) Manufacturing ontology development based on Industry 4.0 demonstration production line. In: 2016 Third International Conference on Trustworthy Systems and their Applications (TSA), IEEE. pp 42–47
He Y, Hao C, Wang Y, Li Y, Wang Y, Huang L (2020) An ontology-based method of knowledge modelling for remanufacturing process planning. J Clean Prod 258:120952
Ostad-Ahmad-Ghorabi H, Rahmani T, Gerhard D (2013) An ontological approach for the integration of life cycle assessment into product data management systems. In: CIRP Design 2012. Springer, London, pp 249–256
AN MM, Romli A, Mohamed R (2021) Eco-ontology for supporting interoperability in product life cycle within product sustainability eco-ontology for supporting interoperability in product life cycle within product sustainability. In: IOP conference in series of materials science engineering
Mohammed M, Romli A, Mohamed R (2021) Using ontology to enhance decision-making for product sustainability in smart manufacturing. In: 2021 international conference on intelligent technology, system and service for internet of everything (ITSS-IoE). IEEE, pp 1–4
Okikiola FM, Ikotun AM, Adelokun AP, Ishola PE (2020) A systematic review of health care ontology. Asian J Res Comput Sci 5(1):15–28
Salman R (2020) Literature review to compare efficiency of various machine learning algorithms in predicting chronic kidney disease (CKD), pp 1–4
Ocker F, Vogel-Heuser B, Paredis CJJ (2022) A framework for merging ontologies in the context of smart factories. Comput Ind 135:103571
Babalou B, König-Ries S (2020) Towards building knowledge by merging multiple ontologies with co merger. arXiv Prepr 2020
Xue X, Yang C, Jiang C, Tsai P, Mao G, Zhu H (2021) Optimizing ontology alignment through linkage learning on entity correspondences. Complexity 1:2021
de Roode M, Fernández-Izquierdo A, Daniele L, Poveda-Villalón M, García-Castro R (2020) SAREF4INMA: a SAREF extension for the industry and manufacturing domain. Semantic Web 11(6):911–926
Li L (2018) China's manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0”. Technol Forecast Social Change 135:66–74
Xingsi X (2019) An automatic biomedical ontology meta-matching technique. J Netw Intell 4(3):109–113
Zhu H, Xue X, Jiang C, Ren H (2021) Multiobjective sensor ontology matching technique with user preference metrics. Wireless Commun Mobile Comput 2021:5594553
Xue X, Wang H, Zhang J, Huang Y, Li M, Zhu H (2021) Matching transportation ontologies with Word2Vec and alignment extraction algorithm. J Adv Transp 2021:4439861
Osman I, Yahia SB, Diallo G (2021) Ontology integration: approaches and challenging issues. Inf Fusion 71:38–63
Salamon JS, Reginato CC, Barcellos MP (2018) Ontology integration approaches: a systematic mapping. In: ONTOBRAS 2018, 161–172
Salamon JS, Reginato CC, Barcellos MP (2018) Ontology integration approaches: a systematic mapping. In: ONTOBRAS, pp 161–172
Mohammed M, Romli A, Mohamed R (2021) Eco-design based on ontology: Historical evolution and research trends. In: AIP Conference Proceedings, vol. 2339, AIP Publishing LLC
Fernández-Izquierdo A, García-Castro R (2022) Ontology verification testing using lexico-syntactic patterns. Inf Sci 582:89–113
Tartir S, Arpinar IB, Sheth AP (2010) Ontological evaluation and validation. In: Theory and applications of ontology: Computer applications. Springer, Dordrecht, pp 115–130
Berners-Lee T, Chen Y, Chilton L, et al (2006) Tabulator: exploring and analyzing linked data on the semantic web. In: Proceedings of the 3rd international semantic web user interaction workshop, vol. 2006, p 159
Fonseca FT, Egenhofer MJ, Davis CA, Borges KAV (2000) Ontologies and knowledge sharing in urban GIS. Comput Environ Urban Syst 24(3):251–272
Lemaignan S, Siadat A, Dantan JY, Semenenko A (2006) MASON: a proposal for an ontology of manufacturing domain. In: IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS'06). IEEE, pp 195–200
Aldana-montes JMJF (2011) Evaluation of two heuristic approaches to solve the ontology meta-matching problem. Knowl Inf Syst 26(2):225–247
Hooi YK, Hassan MF, Shariff AM (2014) A survey on ontology mapping techniques. Adv Comput Sci Appl 2014:829–836
Raunich S, Rahm E (2012) Towards a benchmark for ontology merging. In: OTM Confederated International Conferences “On the Move to Meaningful Internet Systems”. Springer, Berlin, Heidelberg, pp 124–133
Konys A (2018) Knowledge systematization for ontology learning methods. Proc Comput Sci 126:2194–2207
Gracia J, Kernerman I, Bosque-Gil J (2017) Toward linked data-native dictionaries. In: Electronic Lexicography in the 21st Century: Lexicography from Scratch. Proceedings of the eLex 2017 conference, pp 19–21
Lv Y, Xie C (2010) A framework for ontology integration and evaluation. In: 2010 third international conference on intelligent networks and intelligent systems. IEEE, pp 521–524
Châabane S, Jaziri W, Gargouri F (2009) A proposal for a geographic ontology merging methodology. In: 2009 International Conference on the Current Trends in Information Technology (CTIT). IEEE, pp 1–6
Pileggi SF, Crain H, Yahia SB (2020) An ontological approach to knowledge building by data integration. In: International Conference on Computational Science. Springer, Cham, pp 479–493
Stoilos G, Stamou G, Kollias S (2005) A string metric for ontology alignment. In: International semantic web conference. Springer, Berlin, Heidelberg, pp 624–637
Ju SP, Esquivel HE, Rebollar AM, Su MC (2011) CreaDO—A methodology to create domain ontologies using parameter-based ontology merging techniques. In: 2011 10th Mexican International Conference on Artificial Intelligence. IEEE, pp 23–28
Cheatham M, Pesquita C (2017) Semantic data integration. In: Handbook of big data technologies. Springer, Cham, pp 263–305
Solimando A, Guerrini G, Jiménez-ruiz E (2017) Minimizing conservativity violations in ontology alignments: algorithms and evaluation. Knowl Inf Syst 51(3):775–819
Petrov P, Krachunov M, Todorovska E, Vassilev D (2012) An intelligent system approach for integrating anatomical ontologies: an intelligent system approach for integrating anatomical. Biotechnol Equip 26(4):3173–3181
Ndip-agbor E, Cao J, Ehmann K (2018) Towards smart manufacturing process selection in cyber-physical systems. Manuf Lett 17:1–5
Kumar J, Reddy S (2013) Implementation of ontology matching using Protégé. Int J Comput Appl Technol Res 2(6):723–725
Acknowledgements
The research reported in this study is conducted by the researchers at University Malaysia Pahang (UMP), it is funded by FRGS/1/2018/TK10/UMP/02/3 grant. The researchers would like to thank Ministry of Higher Education and UMP for supporting this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mohammed, M., Romli, A., Mohamed, R. (2023). Ontology Integration by Semantic Mapping for Solving the Heterogeneity Problem. In: Al-Emran, M., Al-Sharafi, M.A., Shaalan, K. (eds) International Conference on Information Systems and Intelligent Applications. ICISIA 2022. Lecture Notes in Networks and Systems, vol 550. Springer, Cham. https://doi.org/10.1007/978-3-031-16865-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-16865-9_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16864-2
Online ISBN: 978-3-031-16865-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)