Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

Datil: Learning Fuzzy Ontology Datatypes

  • Conference paper
  • First Online:

Abstract

Real-world applications using fuzzy ontologies are increasing in the last years, but the problem of fuzzy ontology learning has not received a lot of attention. While most of the previous approaches focus on the problem of learning fuzzy subclass axioms, we focus on learning fuzzy datatypes. In particular, we describe theDatil system, an implementation using unsupervised clustering algorithms to automatically obtain fuzzy datatypes from different input formats. We also illustrate the practical usefulness with an application: semantic lifestyle profiling.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11439
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14299
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

References

  1. Alexopoulos, P., Wallace, M., Kafentzis, K., Askounis, D.: IKARUS-Onto: a methodology to develop fuzzy ontologies from crisp ones. Knowl. Inf. Syst.32(3), 667–695 (2012)

    Article  Google Scholar 

  2. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Advanced Applications in Pattern Recognition, 2nd edn. Plenum Press, New York (1987)

    MATH  Google Scholar 

  3. Bobillo, F., Cerami, M., Esteva, F., García-Cerdaña, À., Peñaloza, R., Straccia, U.: Fuzzy description logics. In: Cintula, P., Fermüller, C., Noguera, C. (eds.) Handbook of Mathematical Fuzzy Logic Volume III, Studies in Logic, Mathematical Logic and Foundations, vol. 58, pp. 1105–1181. College Publications (2015). chapter XVI

    Google Scholar 

  4. Bobillo, F., Ruiz, M.D., Gómez-Romero, J., Sánchez, D.: On the application of data mining techniques to graded ontology building. In: Actas del XVIII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2016), pp. 142–143 (2016)

    Google Scholar 

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

    Article MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  7. Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell.17(8), 790–799 (1995)

    Article  Google Scholar 

  8. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell.24, 603–619 (2002)

    Article  Google Scholar 

  9. Díaz-Rodríguez, N., Härmä, A., Helaoui, R., Huitzil, I., Bobillo, F., Straccia, U.: Couch potato or gym addict? Semantic lifestyle profiling with wearables and knowledge graphs. In: Proceedings of the 6th NIPS Workshop on Automated Knowledge Base Construction (AKBC 2017), December 2017

    Google Scholar 

  10. Díaz-Rodríguez, N., León-Cadahía, O., Pegalajar-Cuéllar, M., Lilius, J., Delgado, M.: Handling real-world context-awareness, uncertainty and vagueness in real-time human activity tracking and recognition with a fuzzy ontology-based hybrid method. Sensors14(10), 18131–18171 (2014)

    Article  Google Scholar 

  11. Díaz-Rodríguez, N., Pegalajar-Cuéllar, M., Lilius, J., Delgado, M.: A fuzzy ontology for semantic modelling and recognition of human behaviour. Knowl.-Based Syst.66, 46–60 (2014)

    Article  Google Scholar 

  12. Glimm, B., Horrocks, I., Motik, B., Stoilos, G., Wang, Z.: HermiT: an OWL 2 reasoner. J. Autom. Reason.53(3), 245–269 (2014)

    Article  Google Scholar 

  13. Gómez-Romero, J., Bobillo, F., Ros, M., Molina-Solana, M., Ruiz, M.D., Martín-Bautista, M.J.: A fuzzy extension of the semantic building information model. Autom. Constr.57, 202–212 (2015)

    Article  Google Scholar 

  14. Horridge, M., Bechhofer, S.: The OWL API: a Java API for OWL ontologies. Semant. Web J.2(1), 11–21 (2011)

    Google Scholar 

  15. Iglesias, J., Lehmann, J.: Towards integrating fuzzy logic capabilities into an ontology-based inductive logic programming framework. In: Proceedings of the 11th International Conference on Intelligent Systems Design and Applications (ISDA 2011), pp. 1323–1328 (2011)

    Google Scholar 

  16. Lisi, F.A., Straccia, U.: A logic-based computational method for the automated induction of fuzzy ontology axioms. Fundam. Inform.124(4), 503–519 (2013)

    MathSciNet MATH  Google Scholar 

  17. Lisi, F.A., Straccia, U.: Learning in description logics with fuzzy concrete domains. Fundam. Inform.140(3–4), 373–391 (2015)

    Article MathSciNet  Google Scholar 

  18. Lloyd, S.P.: Least squares quantization in PCM. IEEE Trans. Inf. Theory28(2), 129–137 (1982)

    Article MathSciNet  Google Scholar 

  19. Pires, I.M., Garcia, N.M., Pombo, N., Flrez-Revuelta, F.: From data acquisition to data fusion: a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices. Sensors16(2), 184 (2016)

    Article  Google Scholar 

  20. Staab, S., Studer, R. (eds.): Handbook on Ontologies. IHIS. Springer, Heidelberg (2004).https://doi.org/10.1007/978-3-540-92673-3

    Book MATH  Google Scholar 

  21. Straccia, U.: Foundations of Fuzzy Logic and Semantic Web Languages. CRC Studies in Informatics Series. Chapman & Hall, New York (2013)

    MATH  Google Scholar 

  22. Straccia, U., Mucci, M.: pFOIL-DL: learning (fuzzy)\(\cal{EL}\) concept descriptions from crisp OWL data using a probabilistic ensemble estimation. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing (SAC-15), Salamanca, Spain, pp. 345–352. ACM (2015)

    Google Scholar 

  23. Turlach, B.A.: Bandwidth selection in kernel density estimation: a review. CORE and Institut de Statistique (1993)

    Google Scholar 

  24. W3C OWL Working Group: OWL 2 Web Ontology Language: Document Overview (2008).http://www.w3.org/TR/owl2-overview

  25. Zadeh, L.A.: Fuzzy sets. Inf. Control8(3), 338–353 (1965)

    Article  Google Scholar 

Download references

Acknowledgment

I. Huitzil was partially funded by Universidad de Zaragoza - Santander Universidades (Ayudas de Movilidad para Latinoamericanos - Estudios de Doctorado). N. Díaz-Rodríguez acknowledges AAPELE.eu EU COST Action IC1303 and EU Erasmus+ Funding; part of her work was done during internship at Philips Research. I. Huitzil and F. Bobillo were partially supported by the projects TIN2016-78011-C4-3-R and CUD2017-17. Special thanks are due to Aki Härmä and Rim Helaoui (Philips Research) for their invaluable help with lifestyle real data.

Author information

Authors and Affiliations

  1. I3A, University of Zaragoza, Zaragoza, Spain

    Ignacio Huitzil & Fernando Bobillo

  2. ISTI-CNR, Pisa, Italy

    Umberto Straccia

  3. U2IS, ENSTA ParisTech and Inria FLOWERS, Paris, France

    Natalia Díaz-Rodríguez

Authors
  1. Ignacio Huitzil

    You can also search for this author inPubMed Google Scholar

  2. Umberto Straccia

    You can also search for this author inPubMed Google Scholar

  3. Natalia Díaz-Rodríguez

    You can also search for this author inPubMed Google Scholar

  4. Fernando Bobillo

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toIgnacio Huitzil.

Editor information

Editors and Affiliations

  1. Universidad de Cádiz, Cádiz, Cadiz, Spain

    Jesús Medina

  2. Universidad de Málaga, Málaga, Málaga, Spain

    Manuel Ojeda-Aciego

  3. Universidad de Granada, Granada, Spain

    José Luis Verdegay

  4. Universidad de Granada, Granada, Spain

    David A. Pelta

  5. Universidad de Málaga, Málaga, Málaga, Spain

    Inma P. Cabrera

  6. LIP6, Université Pierre et Marie Curie, CNRS, Paris, France

    Bernadette Bouchon-Meunier

  7. Iona College, New Rochelle, New York, USA

    Ronald R. Yager

Rights and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Huitzil, I., Straccia, U., Díaz-Rodríguez, N., Bobillo, F. (2018). Datil: Learning Fuzzy Ontology Datatypes. In: Medina, J.,et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-319-91476-3_9

Download citation

Publish with us

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11439
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14299
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide -see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

[8]ページ先頭
©2009-2025 Movatter.jp