This study presents a comprehensive meta-survey of 45 comparative studies on ontology development methodologies (ODMs), providing a structured and multidimensional overview of three decades of research. A systematic literature review (SLR) was employed to ensure a rigorous and replicable process for identifying, evaluating, and synthesizing relevant studies. Conducted in two stages, the review first curated core comparative works and then performed detailed qualitative and quantitative analyses, following established SLR frameworks adapted to the objectives of this meta-analysis. The analysis identified 106 distinct ODMs discussed across the selected studies, revealing a marked rise in publication activity between 2014 and 2024, with notable contributions from Malaysia, Germany, the United Kingdom, and India. Most works were published in journals, with 74% available as open access. Commonly evaluated methodologies included METHONTOLOGY, Uschold and King, and Grüninger and Fox, recognized for their influence and ease of use. However, the comparative studies exhibited wide variation in scope, evaluation criteria, and methodological rigor—most relying primarily on qualitative assessments, with only 13% incorporating quantitative analysis. Persistent challenges include limited tool support, incomplete lifecycle coverage, and low accessibility for non-technical users. This is the first comprehensive meta-analysis of comparative ODM research. It introduces an 11-category comparative framework and a revised taxonomy for classifying ODMs based on methodological features. By consolidating fragmented insights, this study offers a unified reference for ontology researchers and practitioners, guiding the selection and design of more systematic, scalable, and user-centric ODMs.
BainDDuttaB. Systematic analysis of COVID-19 ontologies. In: GaroufallouESartoriF (eds) Metadata and semantic research (MTSR 2023), Montpellier, France, 2023. Communications in computer and information science. Vol. 2048. Springer, 2023, pp. 74–91.
7.
SimperlEPBTempichC. Ontology engineering: a reality check. In: MeersmanRTariZHerreroP (eds) On the move to meaningful internet systems (OTM confederated international conferences), Montpellier, France, 29 October–3 November 2006. Cham: Springer, 2006, pp.836–854.
8.
SinhaPKDuttaBVaradarajanU. Ranking the ontology development methodologies using the weighted decision matrix. Data Technologies and Appl2022; 56(5): 686–719. https://doi.org/10.1108/DTA-05-2021-0123
DuttaBChatterjeeUMadalliDP. YAMO: yet another methodology for large-scale faceted ontology construction. J Knowl Manag2015; 19(1): 6–24. https://doi.org/10.1108/JKM-10-2014-0439
11.
Suárez-FigueroaMCGómez-PérezAFernández-LópezM. The NeOn methodology framework: a scenario-based methodology for ontology development. Appl Ontol2015; 10(2): 107–145.
12.
SpoladoreDPessotE. Collaborative ontology engineering methodologies for the development of decision support systems: case studies in the healthcare domain. Electronics2021; 10(9): 1060. https://doi.org/10.3390/electronics10091060
13.
LenatDGuhaRV. Cyc: a midterm report. AI Mag1990; 11(3): 32–59.
14.
IqbalRMuradMAAMustaphaA, et al. An analysis of ontology engineering methodologies: a literature review. Res J Appl Sci Eng Technol2013; 6(16): 2993–3000.
15.
Al-BaltahIAGhaniAAARahmanWNWA, et al. A comparative study on ontology development methodologies towards building semantic conflicts detection ontology for heterogeneous web services. Res J Appl Sci Eng Technol2014; 7(13): 2674–2679. https://doi.org/10.19026/rjaset.7.584
FernándezMGómez-PérezAJuristoN. METHONTOLOGY: from ontological art towards ontological engineering. In: AAAI spring symposium series, Menlo Park, CA, 14 March 1997, pp.33–40. AAAI Press.
21.
GruningerMFoxMS. Methodology for the design and evaluation of ontologies. In: Workshop on basic ontological issues in knowledge sharing (IJCAI-95), Montreal, QC, Canada, 19–20 August 1995, pp.6.1–6.10. AAAI Press.
NoyNFMcGuinnessDL. Ontology development 101: a guide to creating your first ontology. Stanford Knowledge Systems Laboratory Technical Report SMI-2001-0880. Stanford Knowledge Systems Laboratory, 2001.
BouguettayaSPupoFChenM, et al. A meta-survey of generative AI in education: trends, challenges, and research directions. Big Data Cogn Comput2025; 9(9): 237. https://doi.org/10.3390/bdcc9090237
28.
GormanMF. A metasurvey analysis in operations research and management science: a survey of literature reviews. Surveys Oper Res Manag Sci2016; 21(1): 18–28. https://doi.org/10.1016/j.sorms.2016.05.002
29.
AbdellatifMDadamMNVuNT, et al. A step-by-step guide for conducting an umbrella review. Trop Med Health2025; 53(1): 134.
30.
SmithVDevaneDBegleyCM, et al. Methodology in conducting a systematic review of systematic reviews of healthcare interventions. BMC Med Res Methodol2011; 11(1): 15. https://doi.org/10.1186/1471-2288-11-15
31.
PaulJBarariM. Meta-analysis and traditional systematic literature reviews—What, why, when, where, and how?Psychol Mark2022; 39(6): 1099–1115. https://doi.org/10.1002/mar.21657
32.
HaidichAB. Meta-analysis in medical research. Hippokratia2010; 14(Suppl. 1): 29.
33.
JabarMAKhalefaMSAbdullahRH, et al. Meta-analysis of ontology software development process. Int Rev Comput Softw2014; 9(1): 29–37.
SattarASurinESMAhmadMN, et al. Comparative analysis of methodologies for domain ontology development: a systematic review. Int J Adv Comput Sci Appl2020; 11(5): 99–108. http://dx.doi.org/10.14569/IJACSA.2020.0110515
37.
KitchenhamB. Procedures for performing systematic reviews. Keele University, 2004.
38.
ZulkipliZZMaskatRTeoNHI. A systematic literature review of automatic ontology construction. Indones J Electr Eng Comput Sci2022; 28(2): 878–889. http://doi.org/10.11591/ijeecs.v28.i2.pp878-889
39.
ChoiSYKimJAJungR, et al. Analysis and comparison of ontology development methodologies: based on CMM-I version 1.1 maturity level 2 and 3. J KIISE Softw Appl2007; 34(12): 1021–1036.
40.
JidgePGovilkarS. A survey on ontology building methodologies and tools for Indian languages. Int J Comput Sci Inf Technol2016; 7(2): 476–479.
41.
ChimientiMDassistiMDe NicolaA, et al. Evaluation of ontology building methodologies: a method based on balanced scorecards. In: International conference on knowledge engineering and ontology development, Madeira, Portugal, 6–8 October 2009, pp.141–146. SCITEPRESS.
42.
ChimientiMDassistiMDe NicolaA, et al. Benchmarking criteria to evaluate ontology building methodologies. EMOI-INTEROP, 2006. https://ceur-ws.org/Vol-200/14.pdf
43.
UscholdMGruningerM. Ontologies: principles, methods and applications. Knowl Eng Rev1996; 11(2): 93–136.
44.
JonesDMBench-CaponTJMVisserPRS. Methodologies for ontology development. In: 15th international federation for information processing world computer congress, Budapest, Hungary, June 1998, Chapman & Hall, on behalf of the International Federation for Information Processing (IFIP), pp.20–35.
45.
Fernandez-LopezM. Overview of methodologies for building ontologies. In: IJCAI-99 workshop on ontologies and problem-solving methods (KRR5), Stockholm, Sweden, 2 August 1999, CEUR-WS.org, pp.1–13.
46.
Fernandez-LopezMGomez-PerezA. Overview and analysis of methodologies for building ontologies. Knowl Eng Rev2002; 17(2): 129–156. https://doi.org/10.1017/S0269888902000462
47.
CorchoOFernandez-LopezMGomez-PerezA. Methodologies, tools and languages for building ontologies: where is their meeting point?Data Knowl Eng2003; 46(1): 41–64. https://doi.org/10.1016/S0169-023X(02)00195-7
MizoguchiR. Tutorial on ontological engineering part 2: ontology development, tools and languages. New Gener Comput2004; 22: 61–96.
50.
ÖhgrenASandkuhlK. Towards a methodology for ontology development in small and medium-sized enterprises. In: GuimarãesNIzaíasP (eds) International conference on applied computing. DiVA Portal, 2005, pp.369–376.
51.
Casely-HayfordL. A comparative analysis of methodologies, tools and languages used for building ontologies. CCLRC Daresbury Laboratories, 2005.
52.
TempichCPintoHSStaabS. Ontology engineering revisited: an iterative case study. In: European semantic web conference, Budva, Montenegro, 11–14 June 2006, pp.110–124. Springer.
53.
NagypalG. Methodologies for ontology engineering. In: AbeckerAStuderRGrimmS (eds) Semantic web services: concepts, technologies and applications. Springer, 2007, pp.107–134.
54.
BreitmanKKCasanovaMATruszkowskiW. Methods for ontology development. In: BreitmanKKCasanovaMATruszkowskiW (eds) Semantic web: concepts, technologies and applications. Springer, 2007, pp.155–173. https://doi.org/10.1007/978-1-84628-710-7_8
55.
BrusaGCaliuscoMLChiottiO. Towards ontological engineering: a process for building a domain ontology from scratch in public administration. Expert Systems2008; 25(5): 484–503.
56.
DahlemNHahnA. User-friendly ontology creation methodologies: a survey. In: Americas conference on information systems, San Francisco, CA, 6–9 August 2009, Association for Information Systems (AIS), distributed through AIS Electronic Library (AISeL), pp.1–9.
57.
GarciaAO’NeillKGarciaLJ, et al. Developing ontologies within decentralised settings. In: ChenHWangYCheungKH (eds) Semantic e-science. Springer, 2010, pp.99–139.
58.
KapoorBSharmaS. A comparative study ontology building tools for semantic web applications. Int J Web Semant Technol2010; 1(3): 1–13.
59.
IslamNAbbasiAZShaikhZA. Semantic web: choosing the right methodologies, tools and standards. In: International conference on information and emerging technologies, Karachi, Pakistan, 14–16 June 2010, pp.1–5. IEEE.
60.
KehagiasDDKontotasiouDTzovarasD. Evaluation framework for ontology development and management methodologies. In: ORES-2010, Hersonissos, Crete, Greece, 31 May 2010, p.134. ORES.
LeungNKLauSKTsangN. An ontology development methodology to integrate existing ontologies in an ontology development process. Communications of the ICISA2013; 13(2): 31–61.
63.
ChangruiYYanL. Comparative research on methodologies for domain ontology development. In: HuangDSGanYGuptaP, et al. (eds) International conference on intelligent computing. Springer, 2011, pp.349–356.
64.
StadlhoferBSalhoferPDurlacherA. An overview of ontology engineering methodologies in the context of public administration. In: CheptsovA (ed.) 7th international conference on advances in semantic processing. Vol. 29. IARIA, 2013, pp.36–42.
RekhaRVSyamiliC. Ontology engineering methodologies: an analytical study. In: 11th International CALIBER, Anna University, Chennai, India, 2–4 August 2017, pp.193–199. INFLIBNET Centre.
68.
JohnSShahNStewartC, et al. Software centric innovative methodology for ontology development. In: 9th international joint conference on knowledge discovery, knowledge engineering and knowledge management (KEOD 2017), Funchal, Portugal, November 2017, pp.139–146. SciTePress, Science and Technology Publications. https://doi.org/10.5220/0006482901390146
69.
KeetM. An introduction to ontology engineering. College Publications, 2018, p.344.
70.
YuniantaABasoriAHPrabuwonoAS, et al. OntoDI: the methodology for ontology development on data integration. Int J Adv Comput Sci Appl2019; 10(1): 160–168. https://doi.org/10.14569/IJACSA.2019.0100121
KotisKIVourosGASpiliotopoulosD. Ontology engineering methodologies for the evolution of living and reused ontologies: status, trends, findings and recommendations. Knowl Eng Rev2020; 35: E4. https://doi.org/10.1017/S0269888920000065
73.
AminuEFOyefolahanIOAbdullahiMB, et al. A review on ontology development methodologies for developing ontological knowledge representation systems for various domains. Int J Inf Eng Electron Bus2020; 12(2): 28–39. https://doi.org/10.5815/ijieeb.2020.02.05
74.
SmirnovALevashovaTPonomarevA, et al. Methodology for multi-aspect ontology development: ontology for decision support based on human-machine collective intelligence. IEEE Access2021; 9: 135167–135185. https://doi.org/10.1109/ACCESS.2021.3116870
75.
AlfaifiY. Ontology development methodology: a systematic review and case study. In: 2nd international conference on computing and information technology (ICCIT), Tabuk, Saudi Arabia, 25–27 January 2022, pp.446–450. IEEE. https://doi.org/10.1109/ICCIT52419.2022.9711664
76.
SpoladoreDPessotE. An evaluation of agile ontology engineering methodologies for the digital transformation of companies. Comput Ind2022; 140: 103690. https://doi.org/10.1016/j.compind.2022.103690
77.
ElHassouniJQadiAE. Ontology engineering methodologies: state of the art. In: LazaarMDuvalletCTouhafiA, et al. (eds) 5th international conference on big data and internet of things (BDIoT 2021), Vol. 489. Springer, 2022, pp.59–72. https://doi.org/10.1007/978-3-031-07969-6_5
78.
HaridySIsmailRMBadrN, et al. An ontology development methodology based on ontology-driven conceptual modeling and natural language processing: tourism case study. Big Data Cogn Comput2023; 7(2): 101. https://doi.org/10.3390/bdcc7020101
79.
SpoladoreDPessotETrombettaA. A novel agile ontology engineering methodology for supporting organizations in collaborative ontology development. Comput Ind2023; 151: 103979. https://doi.org/10.1016/j.compind.2023.103979
80.
WeberSDannenTStauderL, et al. Methodology for agile and iterative ontology development for toolmaking. Procedia CIRP2023; 120: 529–534. https://doi.org/10.1016/j.procir.2023.09.032
81.
Mohd NasirAHSulaimanMFKok LeongL, et al. An approach for developing an ontology: learned from business model ontology design and development. Int J Adv Comput Sci Appl2024; 15(3): 539–554. https://doi.org/10.14569/ijacsa.2024.0150355
82.
CampbellDPicard-AitkenMCôtéG, et al. Bibliometrics as a performance measurement tool for research evaluation: the case of research funded by the National Cancer Institute of Canada. Am J Eval2010; 31(1): 66–83. https://doi.org/10.1177/1098214009354774
LimaJFAmaralCMGMolinaroLFR. Ontology: an analysis of the literature. In: Quintela VarajãoJECruz-CunhaMMPutnikGD, et al. (eds) International conference on enterprise information systems. Springer, 2010, pp.426–435.
87.
SyamiliCRekhaRV. Trend of research on ontology: a study based on ProQuest and DART. In: International conference on knowledge modelling and knowledge management, documentation research and training, Bangalore, India, December 2013, pp.161–171. Bangalore Centre.
GruberTR. Toward principles for the design of ontologies used for knowledge sharing. Int J Hum Comput Stud1995; 43(5–6): 907–928.
91.
StaabSStuderRSchnurrHP, et al. Knowledge processes and ontologies. IEEE Intell Syst2001; 16(1): 26–34.
92.
The KACTUS Booklet version 10. Esprit Project 8145, September1996.
93.
SpynsPTangYMeersmanR. An ontology engineering methodology for DOGMA. Applied Ontology2008; 3(1–2): 13–39.
94.
AuerSHerreH. RapidOWL—an agile knowledge engineering methodology. In: VirbitskaiteIVoronkov (eds) International Andrei Ershov memorial conference on perspectives of system informatics. Springer, 2006, pp.424–430.
95.
FarquharAFikesRPrattW, et al. Collaborative ontology construction for information integration. Technical Report KSL 95-63. Knowledge Systems Laboratory, Stanford University, 1995.
96.
MarsNJITer StalWGDe JongH, et al. Semi-automatic knowledge acquisition in Plinius: an engineering approach. In: 8th Banff knowledge acquisition for knowledge-based systems workshop, Banff, AB, Canada, 30 January–4 February 1994, pp.41–415. SRDG Publications.
97.
GiunchigliaFDuttaBMalteseV, et al. A facet-based methodology for the construction of a large-scale geospatial ontology. J Data Semant2012; 1(1): 57–73. https://doi.org/10.1007/s13740-012-0005-x
98.
AlexopoulosPWallaceMKafentzisK, et al. IKARUS-Onto: a methodology to develop fuzzy ontologies from crisp ones. Knowl Inf Syst2012; 32(3): 667–695.
YunHXuJXiongJ, et al. A knowledge engineering approach to develop domain ontology. Int J Distance Educ Technol2011; 9(1): 57–71.
101.
De MoorADe LeenheerPMeersmanR. DOGMA-MESS: a meaning evolution support system for interorganizational ontology engineering. In: International conference on conceptual structures (ICCS 2006), Aalborg, Denmark, 16–21 July 2006, pp.189–202. Springer.
102.
PeroniS. A simplified agile methodology for ontology development. In: DragoniMPoveda-VillalónMJimenez-RuizE (eds) OWL: Experiences and Directions—Reasoner Evaluation (OWLED ORE 2016). Vol. 10161. Springer, 2017, pp.55–69.
103.
PresuttiVDagaEGangemiA, et al. eXtreme design with content ontology design patterns. In: Workshop on ontology patterns, 2009, pp.83–97. https://ceur-ws.org/Vol-516/pap21.pdf
104.
PatelASMerlinoGPuliafitoA, et al. An NLP-guided ontology development and refinement approach to represent and query visual information. Expert Syst Appl2023; 213: 118998. https://doi.org/10.1016/j.eswa.2022.118998
GuimarãesNCDe CarvalhoCL. A modular framework for ontology learning from text in Portuguese. Multi Sci J2020; 3: 37–42. https://doi.org/10.33837/msj.v3i3.899
107.
DeraEFrasincarFSchoutenK, et al. SASOBUS: semi-automatic sentiment domain ontology building using synsets. In: European semantic web conference (ESWC 2020), Crete, Greece, 31 May–4 June 2020, pp.105–120. Springer.
108.
MilanifardOKahaniM. Proposing an integrated multi source ontology construction methodology. Comput Knowl Eng2020; 3: 11–24. https://doi.org/10.22067/cke.2020.39289
109.
RawsthorneHMAbadieNKergosienE, et al. ATONTE: towards a new methodology for seed ontology development from texts and experts. In: 23rd international conference on knowledge engineering and knowledge management (EKAW 2022), Bolzano, Italy, 26–29 September 2022, pp.1–6. Springer.
110.
PolenghiARodaIMacchiM, et al. Knowledge reuse for ontology modelling in maintenance and industrial asset management. J Ind Inf Integ2022; 27: 100298. https://doi.org/10.1016/j.jii.2021.100298
111.
MahmoodKMokhtarRRazaMA, et al. Ecological and confined domain ontology construction scheme using concept clustering for knowledge management. Appl Sci2023; 13: 32. https://doi.org/10.3390/app13010032
112.
AlaaRGawishMFernández-VeigaM. Improving recommendations for online retail markets based on ontology evolution. Electronics2021; 10: 1650. https://doi.org/10.3390/electronics10141650
LippolisASSaeedizadeMJKeskisärkkäR, et al. Ontology generation using large language models. In: CurryEAcostaMPoveda-VillalónM, et al. (eds) European semantic web conference. Springer, 2025, pp.321–341.
