Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

A comprehensive review of type-2 fuzzy Ontology

Abstract

Ontologies are not only crucial for extending the traditional web into the Semantic Web but also for developing intelligent applications, by converting the raw data into smart data, through semantic enrichment. However, crisp Ontologies are not able to represent fuzzy knowledge which is often encountered in real-world applications. Fuzzy Ontology introduces fuzzy logical rules in Ontology for representing imprecise domain concepts such as darkness, hotness, thickness, creamy etc. in a machine-readable and interoperable format. The performance of fuzzy Ontology decreases with the increase of fuzziness in the domain knowledge. Type-2 fuzzy Ontologies (T2FO) were introduced to represent the domain knowledge where the concepts are either extremely vague or their vagueness increases gradually. The type-2 fuzzy Ontology domain is continuously expanding and there is a need to provide a comprehensive review incorporating the literature of T2FO development approaches, its applications in different domains, reasoners developed for inferencing on type-2 fuzzy Ontology, and evaluation approaches. To perform a comprehensive survey about the T2FO, we used Google Scholar as the main literature research tool to review papers published between 1998 to 2018. We then summarized the published approaches by comparing their features proposed for T2FO development, reasoning or inference, and evaluation approaches. This paper also identifies the domains wherein the past T2FO has been used to develop real-world applications. We conclude this paper by summarizing the previous work, and by identifying the research gaps for investigators.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2

References

  1. Abacha AB, Zweigenbaum P (2011) Automatic extraction of semantic relations between medical entities: a rule based approach. J Biomed Semant 2(5):S4

  2. Abburu S (2012) A survey on ontology reasoners and comparison. Int J Comput Appl 57(17):656

  3. Acampora G (2013) Fuzzy markup language: a XML based language for enabling full interoperability in fuzzy systems design. On the power of fuzzy markup language. Springer, pp 17–31

  4. Ali F, Kim EK, Kim Y-G (2015a) Type-2 fuzzy ontology-based opinion mining and information extraction: a proposal to automate the hotel reservation system. Appl Intell 42(3):481–500

  5. Ali F, Kim EK, Kim Y-G (2015b) Type-2 fuzzy ontology-based semantic knowledge for collision avoidance of autonomous underwater vehicles. Inf Sci 295:441–464

  6. Ali F, Islam SR, Kwak D, Khan P, Ullah N, Yoo S-J, Kwak KS (2018) Type-2 fuzzy ontologyaided recommendation systems for IoT based healthcare. Comput Commun 119:138–155

  7. Alobaidi M, Malik KM, Hussain M (2018a) Automated ontology generation framework powered by linked biomedical ontologies for disease-drug domain. Comput Methods Programs Biomed 165:117–128

  8. Alobaidi M, Malik KM, Sabra S (2018b) Linked open data-based framework for automatic biomedical ontology generation. BMC Bioinform 19(1):319

  9. Antoniou G, Van Harmelen F (2009) Web ontology language: OWL. In: Handbook on ontologies. Springer, pp 91–110

  10. Baader F, Sattler U (2001) An overview of tableau algorithms for description logics. Stud Log 69(1):5–40

  11. Baader F, Lutz C, Suntisrivaraporn B (2006) CELa polynomial-time reasoner for life science ontologies. In: International joint conference on automated reasoning. Springer

  12. Bahri A, Bouziz R, Gargouri F (2010) A generalized fuzzy extension of EL++. In: 2010 annual meeting of the North American fuzzy information processing society (NAFIPS). IEEE

  13. Berners-Lee T, Hendler J, Lassila O (2001) The semantic web. Sci Am 284(5):34–43

  14. Bobillo F, Straccia U (2009) An OWL ontology for fuzzy OWL 2. In: International symposium on methodologies for intelligent systems. Springer

  15. Bobillo F, Straccia U (2011a) Fuzzy ontology representation using OWL 2. Int J Approx Reason 52(7):1073–1094

  16. Bobillo F, Straccia U (2011b) Reasoning with the finitely many-valued ukasiewicz fuzzy description logic SROIQ. Inf Sci 181(4):758–778

  17. Bobillo F, Straccia U (2016) The fuzzy ontology reasoner fuzzyDL. Knowl Based Syst 95:12–34

  18. Bobillo F, Straccia U (2017) Generalizing type-2 fuzzy ontologies and type-2 fuzzy description logics. Int J Approx Reason 87:40–66

  19. Bobillo F, Delgado M, Gmez-Romero J (2012) DeLorean: a reasoner for fuzzy OWL 2. Expert Syst Appl 39(1):258–272

  20. Bobillo F, Delgado M, Gmez-Romero J (2008) Optimizing the crisp representation of the fuzzy description logic \(\cal S\it \cal R\it \cal O\it \cal I\it \cal Q\it \). In: Uncertainty reasoning for the semantic web I. Springer, pp 189–206

  21. Bobillo F, Straccia U (2008) fuzzyDL: an expressive fuzzy description logic reasoner. In: IEEE international conference on fuzzy systems, 2008. FUZZ-IEEE 2008 (IEEE world congress on computational intelligence). IEEE

  22. Bobillo F, Straccia U (2010) Representing fuzzy ontologies in OWL 2. In: 2010 IEEE international conference on fuzzy systems (FUZZ). IEEE

  23. Buitelaar P, Cimiano P, Frank A, Hartung M, Racioppa S (2008) Ontology-based information extraction and integration from heterogeneous data sources. Int J Hum Comput Stud 66(11):759–788

  24. Bukhari A C, Baker C J (2013) The Canadian health census as Linked Open Data: towards policy making in public health. Data integration in the life sciences

  25. Bukhari A C, Klein A, Baker C J (2013) Towards interoperable bioNLP semantic web services using the SADI framework. In: International conference on data integration in the life sciences. Springer

  26. Bukhari A C, Nagy M L, Krauthammer M, Ciccarese P, Baker C J (2015) BIM: an open ontology for the annotation of biomedical images. ICBO

  27. Bukhari AC, Kim Y-G (2011) Exploiting the heavyweight ontology with multi-agent system using vocal command system: a case study on e-mall. Int J Adv Comput Technol 3(6):233–241

  28. Bukhari AC, Kim Y-G (2011) Incorporation of fuzzy theory with heavyweight ontology and its application on vague information retrieval for decision making. Int J Fuzzy Log Intell Syst 11(3):171–177

  29. Bukhari AC, Kim Y-G (2012) Integration of a secure type-2 fuzzy ontology with a multi-agent platform: a proposal to automate the personalized flight ticket booking domain. Inf Sci 198:24–47

  30. Bukhari SAC, Martnez-Romero M, OConnor MJ, Egyedi AL, Willrett D, Graybeal J, Musen MA, Cheung K-H, Kleinstein SH (2018) CEDAROnDemand: a browser extension to generate ontology-based scientificmetadata. BMC Bioinform 19(1):268

  31. Calegari S, Ciucci D (2006) Integrating fuzzy logic in ontologies. In: ICEIS (2)

  32. Calvanese D, De Giacomo G, Lenzerini M, Nardi D (2001) Reasoning in expressive description logics. In: Handbook of automated reasoning, vol 2, pp 1581–1634

  33. Chen L, Liu C, Zhang X, Wang S, Strasunskas D, Tomassen SL, Rao J, Li W-S, Candan KS, Chiu DK (2009) Advances in web and network technologies and information management: AP Web/WAIM 2009 international workshops: WCMT 2009, RTBI 2009, DBIR-ENQOIR 2009, and PAIS 2009. Springer

  34. Choi N, Song I-Y, Han H (2006) A survey on ontology mapping. ACM SIGMOD Rec 35(3):34–41

  35. Corcho O, Fernandez-Lopez M, Gomez-Perez A (2003) Methodologies, tools and languages for building ontologies. Where is their meeting point? Data Knowl Eng 46(1):41–64

  36. Del Carmen Legaz-Garca M, Miarro-Gimnez JA, Menrguez-Tortosa M, Fernndez-Breis JT (2016) Generation of open biomedical datasets through ontology-driven transformation and integration processes. J Biomed Semant 7(1):32

  37. Dellschaft K, Staab S (2008) Strategies for the evaluation of ontology learning. Ontol Learn Popul 167:253–272

  38. Dentler K, Cornet R, Ten Teije A, De Keizer N (2011) Comparison of reasoners for large ontologies in the OWL 2 EL profile. Semant Web 2(2):71–87

  39. Gangemi A, Catenacci C, Ciaramita M, Lehmann J (2006) Modelling ontology evaluation and validation. European semantic web conference. Springer

  40. Garca-Pealvo FJ, Colomo-Palacios R, Garca J, Thern R (2012) Towards an ontology modeling tool. A validation in software engineering scenarios. Expert Syst Appl 39(13):11468–11478

  41. Gatial E, Balogh Z, Laclavik M, Ciglan M, Hluchy L (2005) Focused web crawling mechanism based on page relevance. In: Proceedings of ITAT, pp 41–46

  42. Gauch S, Chaffee J, Pretschner A (2003) Ontology-based personalized search and browsing. Web Intell Agent Syst Int J 1(3,4):219–234

  43. Ghorbel H, Bahri A, Bouaziz R (2009) Fuzzy protg for fuzzy ontology models. Age 12(18):30

  44. Gibbins N, Shadbolt N (2009) Resource description framework (RDF)

  45. Glimm B, Horrocks I, Motik B, Stoilos G (2009) HermiT: reasoning with large ontologies. Computing Laboratory, Oxford University, Oxford

  46. Gmez-Romero J, Bobillo F, Ros M, Molina-Solana M, Ruiz MD, Martn-Bautista M (2015) A fuzzy extension of the semantic Building Information Model. Autom Constr 57:202–212

  47. Gonalves MA, Fox EA, Watson LT (2008) Towards a digital library theory: a formal digital library ontology. Int J Digit Libr 8(2):91–114

  48. Gruber TR (1995) Toward principles for the design of ontologies used for knowledge sharing? Int J Hum Comput Stud 43(5–6):907–928

  49. Haarslev V, Mller R (2000) Consistency testing: the RACE experience. In: International conference on automated reasoning with analytic tableaux and related methods. Springer

  50. Haarslev V, Pai H-I, Shiri N (2007) Optimizing tableau reasoning in ALC extended with uncertainty. Description Logics

  51. Haase P, Lewen H, Studer R, Tran D T, Erdmann M, dAquin M, Motta E (2008) The neon ontology engineering toolkit. WWW

  52. Habiballa H (2007) Resolution strategies for fuzzy description logic. In: EUSFLAT conference (2)

  53. Hartmann J, Spyns P, Giboin A, Maynard D, Cuel R,Surez-Figueroa M C, Sure Y (2005) D1. 2.3 Methods for ontology evaluation. EU-IST Network of Excellence (NoE) IST-2004-507482KWEB Deliverable D 1

  54. Horrocks I, Sattler U (2007) A tableau decision procedure for \(\cal{SHOIQ} \). J Autom Reason 39(3):249–276

  55. Horrocks I, Patel-Schneider PF, Boley H, Tabet S, Grosof B, Dean M (2004) SWRL: a semantic web rule language combining OWL and RuleML. W3C Memb Submiss 21:79

  56. Horrocks I, Kutz O, Sattler U (2006) The even more irresistible SROIQ. Kr 6:57–67

  57. Huang H-D, Lee C-S, Hagras H, Kao H-Y (2012) TWMAN+: a type-2 fuzzy ontology model for malware behavior analysis. In: 2012 IEEE international conference on systems, man, and cybernetics (SMC). IEEE

  58. Huang H-D, Lee C-S, Wang M-H, Kao H-Y (2014) IT2FS-based ontology with soft-computing mechanism for malware behavior analysis. Soft Comput 18(2):267–284

  59. Hudelot C, Atif J, Bloch I (2008) Fuzzy spatial relation ontology for image interpretation. Fuzzy Sets Syst 159(15):1929–1951

  60. Huo L, Ouyang J, Liu D (2010) Interval-valued fuzzy description logic IFALCN Preliminary results. In: 2010 IEEE international conference on intelligent computing and intelligent systems (ICIS). IEEE

  61. Ivanova T I (2008) A metic and approach for fuzzy ontology evaluation. In: Proceedings of international scientific conference computer science

  62. Jiang Y-C, Shi Z-Z, Tang Y, Wang J (2007) Fuzzy description logic for semantics representation of the semantic web. Ruan Jian Xue Bao (J Softw) 18(6):1257–1269

  63. Kazakov Y, Krtzsch M, Simancik F (2012) ELK reasoner: architecture and evaluation. ORE

  64. Klir G, Yuan B (1995) Fuzzy sets and fuzzy logic. Prentice Hall, New Jersey

  65. Lawley M J, Bousquet C (2010) Fast classification in Protg: Snorocket as an OWL 2 EL reasoner. In: Proceedings of 6th Australasian Ontology Workshop (IAOA10). Conferences in research and practice in information technology

  66. Lee C-S, Wang M-H, Hong T-P, Chaslot G, Hoock J-B, Rimmel A, Teytaud O, Kuo Y-H (2009) A novel ontology for computer Go knowledge management. In: IEEE international conference on fuzzy systems, 2009. FUZZ-IEEE 2009. IEEE

  67. Lee C-S, Wang M-H, Yan Z-R, Chen Y-J, Doghmen H, Teytaud O (2010c) FML-based type-2 fuzzy ontology for computer Go knowledge representation. In: 2010 International conference on system science and engineering (ICSSE). IEEE

  68. Lee, C-S, Wang M-H, Wu M-H, Hsu C-Y, Lin Y-C, Yen S-J (2010b) A type-2 fuzzy personal ontology for meeting scheduling system. In: 2010 IEEE international conference on fuzzy systems (FUZZ). IEEE

  69. Lee C-S, Wang M-H (2011) A fuzzy expert system for diabetes decision support application. IEEE Trans Syst Man Cybern Part B Cybern 41(1):139–153

  70. Lee C-S, Jian Z-W, Huang L-K (2005) A fuzzy ontology and its application to news summarization. IEEE Trans Syst Man Cybern Part B (Cybernetics) 35(5):859–880

  71. Lee C-S, Jiang C-C, Hsieh T-C (2006) A genetic fuzzy agent using ontology model for meeting scheduling system. Inf Sci 176(9):1131–1155

  72. Lee C-S, Wang M-H, Hagras H (2010a) A type-2 fuzzy ontology and its application to personal diabetic-diet recommendation. IEEE Trans Fuzzy Syst 18(2):374–395

  73. Lee CS, Wang MH, Acampora G, Hsu CY, Hagras H (2010d) Diet assessment based on type2 fuzzy ontology and fuzzy markup language. Int J Intell Syst 25(12):1187–1216

  74. Li R, Wen K, Gu X, Li Y, Sun X, Li B (2011) Type-2 fuzzy description logic. Front Comput Sci China 5(2):205–215

  75. Liu B, Chen-Chuan-Chang K (2004) Special issue on web content mining. ACM SIGKDD Explor Newslett 6(2):1–4

  76. Ma Z, Zhang F, Wang H, Yan L (2013) An overview of fuzzy description logics for the semantic web. Knowl Eng Rev 28(1):1–34

  77. Magka D, Krtzsch M, Horrocks I (2014) A rule-based ontological framework for the classification of molecules. J Biomed Semant 5(1):17

  78. Mahmood K, Raza A, Krishnamurthy M, Takahashi H (2016) Autonomous decentralized semantic-based architecture for dynamic content classification. IEICE Trans Commun 99(4):849–858

  79. Mazzieri M, Dragoni AF (2008) A fuzzy semantics for the resource description framework. In: Uncertainty reasoning for the semantic web I. Springer, pp 244–261

  80. Mazzieri M, Dragoni A F, Marche U (2005) A fuzzy semantics for semantic web languages. ISWC-URSW

  81. McGuinness DL, Van Harmelen F (2004) OWL web ontology language overview. W3C Recomm 10(10):2004

  82. Mendel JM, John RI, Liu F (2006) Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14(6):808–821

  83. Mezei J, Wikstrm R, Carlsson C (2015) Aggregating linguistic expert knowledge in type-2 fuzzy ontologies. Appl Soft Comput 35:911–920

  84. Mi Z-S, Bukhari AC, Kim Y-G (2014) Anobstacle recognizing mechanism for autonomous underwater vehiclespowered by fuzzy domain ontology and support vector machine. Math Probl Eng 2014:676729

  85. Miller E (1998) An introduction to the resource description framework. Bull Am Soc Inf Sci Technol 25(1):15–19

  86. Noy NF, Sintek M, Decker S, Crubzy M, Fergerson RW, Musen MA (2001) Creating semantic web contents with protege-2000. IEEE Intell Syst 16(2):60–71

  87. Parry D (2006) Evaluation of a fuzzy ontology-based medical information system. Int J Healthc Inf Syst Inform (IJHISI) 1(1):40–51

  88. Parsia B, Sirin E (2004) Pellet: an owl dl reasoner. In: Third international semantic web conference-poster, Publishing

  89. Plessers P, De Troyer O (2005) Ontology change detection using a version log. In: International semantic web conference. Springer

  90. Poesio M, Barbu E, Giuliano C, Romano L, Kessler F B (2008) Supervised relation extraction for ontology learning from text based on a cognitively plausible model of relations. ECAI 2008 3rd workshop on ontology learning and population

  91. Reiss F, Raghavan S, Krishnamurthy R, Zhu H, Vaithyanathan S (2008) An algebraic approach to rule-based information extraction. In: IEEE 24th international conference on data engineering, 2008. ICDE 2008. IEEE

  92. Sabra S, Malik KM, Alobaidi M (2018) Prediction of venous thromboembolism using semantic and sentiment analyses of clinical narratives. Comput Biol Med 94:1–10

  93. Sanchez E (2006) Fuzzy logic and the semantic web. Elsevier, Amsterdam

  94. Schmidt M, Hornung T, Lausen G, Pinkel C (2009) SP2Bench: a SPARQL performance benchmark. In: IEEE 25th international conference on data engineering, 2009. ICDE’09. IEEE

  95. Sean B P (2001) The semantic web: an introduction

  96. Snow R, Jurafsky D, Ng AY (2005) Learning syntactic patterns for automatic hypernym discovery. Advances in neural information processing systems

  97. Stoilos G, Simou N, Stamou G, Kollias S (2006) Uncertainty and the semantic web. IEEE Intell Syst 21(5):84–87

  98. Stoilos G, Stamou G, Pan JZ (2010) Fuzzy extensions of OWL: logical properties and reduction to fuzzy description logics. Int J Approx Reason 51(6):656–679

  99. Stoilos G, Stamou G B (2007) Extending fuzzy description logics for the semantic web. OWLED

  100. Straccia U (2004) Transforming fuzzy description logics into classical description logics. European workshop on logics in artificial intelligence. Springer

  101. Straccia U (2009a) A minimal deductive system for general fuzzy RDF. In: International conference on web reasoning and rule systems. Springer

  102. Straccia U (2009b) Softfacts: a top-k retrieval engine for a tractable description logic accessing relational databases. ISTI-CNR, Techical Report

  103. Straccia U (2001) Reasoning within fuzzy description logics. J Artif Intell Res 14:137–166

  104. Straccia U (2006) A fuzzy description logic for the semantic web. Capturing Intell 1:73–90

  105. Tho QT, Hui SC, Fong ACM, Cao TH (2006) Automatic fuzzy ontology generation for semantic web. IEEE Trans Knowl Data Eng 18(6):842–856

  106. Thomas E, Pan JZ, Ren Y (2010) TrOWL: tractable OWL 2 reasoning infrastructure. Extended semantic web conference. Springer

  107. Tommila T, Hirvonen J, Pakonen A (2010) Fuzzy ontologies for retrieval of industrial knowledge—a case study. VTT working papers 153

  108. Tsarkov D, Horrocks I (2006) FaCT++ description logic reasoner: system description. In: International joint conference on automated reasoning. Springer

  109. Tsatsou D, Dasiopoulou S, Kompatsiaris I, Mezaris V (2014) LiFR: a lightweight fuzzy DL reasoner. European semantic web conference. Springer

  110. Vrandei D, Sure Y (2007) How to design better ontology metrics. European semantic web conference. Springer

  111. Wikstrm R, Mezei J (2015) Intrusion detection with type-2 fuzzy ontologies and similarity measures. Intelligent methods for cyber warfare. Springer, pp 151–172

  112. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J-W, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJG, Groth P, Goble C, Grethe JS, Heringa J, t Hoen PAC, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone S-A, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, vander Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data 3:160018

  113. Yi S, Dezheng Z, Li C (2010) Fuzzy ontology constructing and its application in traditional Chinese medicine. In: 2010 IEEE international conference on intelligent computing and intelligent systems (ICIS). IEEE

  114. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

  115. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning. Inf Sci 8(3):199–249

  116. Zhai D, Mendel JM (2011) Uncertainty measures for general type-2 fuzzy sets. Inf Sci 181(3):503–518

  117. Zhang F, Cheng J, Ma Z (2016) A survey on fuzzy ontologies for the semantic web. Knowl Eng Rev 31(3):278–321

Download references

Author information

Correspondence to Syed Ahmad Chan Bukhari.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Qasim, I., Alam, M., Khan, S. et al. A comprehensive review of type-2 fuzzy Ontology. Artif Intell Rev 53, 1187–1206 (2020). https://doi.org/10.1007/s10462-019-09693-9

Download citation

Keywords

  • Ontology
  • Fuzzy Ontology
  • Type-2 fuzzy Ontology
  • Fuzzy knowledge