2018
Conference article
Open Access
Datil: learning fuzzy ontology datatypes
Huitzil I., Straccia U., Diaz-Rodriguez N., Bobillo F.
[INFO]Computer Science [cs] Description logics [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] Machine learning [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Description Logics Lifestyle profiling Clustering Fuzzy Logic Fuzzy ontologies
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 the Datil 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.
Source: IPMU 2018 - International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 100–112, Cádiz, Spain, 11-15 June, 2018
Publisher: Springer-Verlag, Berlin, DEU
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12. Glimm, B., Horrocks, I., Motik, B., Stoilos, G., Wang, Z.: HermiT: An OWL 2 reasoner. Journal of Automated Reasoning 53(3), 245{269 (2014)
13. Gomez-Romero, J., Bobillo, F., Ros, M., Molina-Solana, M., Ruiz, M.D., Mart nBautista, M.J.: A fuzzy extension of the semantic building information model. Automation in Construction 57, 202{212 (2015)
14. Horridge, M., Bechhofer, S.: The OWL API: A Java API for OWL ontologies. Semantic Web Journal 2(1), 11{21 (2011)
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16. Lisi, F.A., Straccia, U.: A logic-based computational method for the automated induction of fuzzy ontology axioms. Fundamenta Informaticae 124(4), 503{519 (2013)
17. Lisi, F.A., Straccia, U.: Learning in description logics with fuzzy concrete domains. Fundamenta Informaticae 140(3{4), 373{391 (2015)
18. Lloyd., S.P.: Least squares quantization in PCM. IEEE Transactions on Information Theory 28(2), 129{137 (1982)
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 identi cation of activities of daily living using mobile devices. Sensors 16(2), 184 (2016)
20. Staab, S., Studer, R. (eds.): Handbook on Ontologies. International Handbooks on Information Systems, Springer (2004)
21. Straccia, U.: Foundations of Fuzzy Logic and Semantic Web Languages. CRC Studies in Informatics Series, Chapman & Hall (2013)
22. Straccia, U., Mucci, M.: pFOIL-DL: Learning (fuzzy) 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). pp. 345{352. ACM, Salamanca, Spain (2015)
23. Turlach, B.A.: Bandwidth selection in kernel density estimation: A review. In: CORE and Institut de Statistique (1993)
24. W3C OWL Working Group: OWL 2 Web Ontology Language: Document Overview (2008), [Online] Available: http://www.w3.org/TR/owl2-overview
25. Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338{353 (1965)
2. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Advanced Applications in Pattern Recognition, Plenum Press, 2nd edn. (1987)
3. Bobillo, F., Cerami, M., Esteva, F., Garc a-Cerdan~a, A., Pen~aloza, R., Straccia, U.: Fuzzy description logics. In: Cintula, P., Fermuller, C., Noguera, C. (eds.) Handbook of Mathematical Fuzzy Logic Volume III, Studies in Logic, Mathematical Logic and Foundations, vol. 58, chap. XVI, pp. 1105{1181. College Publications (2015)
4. Bobillo, F., Ruiz, M.D., Gomez-Romero, J., Sanchez, D.: On the application of data mining techniques to graded ontology building. In: Actas del XVIII Congreso Espan~ol sobre Tecnolog as y Logica Fuzzy (ESTYLF 2016). pp. 142{143 (2016)
5. Bobillo, F., Straccia, U.: Fuzzy ontology representation using OWL 2. International Journal of Approximate Reasoning 52(7), 1073{1094 (2011)
6. Bobillo, F., Straccia, U.: The fuzzy ontology reasoner fuzzyDL. Knowledge-Based Systems 95, 12{34 (2016)
7. Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(8), 790{799 (1995)
8. Comaniciu, D., Meer, P., Member, S.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603{619 (2002)
9. D az-Rodr guez, N., Harma, A., Helaoui, R., Huitzil, I., Bobillo, F., Straccia, U.: Couch potato or gym addict? Semantic lifestyle pro ling with wearables and knowledge graphs. In: Proceedings of the 6th NIPS Workshop on Automated Knowledge Base Construction (AKBC 2017) (December 2017)
10. D az-Rodr guez, N., Leon-Cadah a, O., Pegalajar-Cuellar, M., Lilius, J., Delgado, M.: Handling real-world context-awareness, uncertainty and vagueness in realtime human activity tracking and recognition with a fuzzy ontology-based hybrid method. Sensors 14(10), 18131{18171 (2014)
11. D az-Rodr guez, N., Pegalajar-Cuellar, M., Lilius, J., Delgado, M.: A fuzzy ontology for semantic modelling and recognition of human behaviour. Knowledge-Based Systems 66, 46{60 (2014)
12. Glimm, B., Horrocks, I., Motik, B., Stoilos, G., Wang, Z.: HermiT: An OWL 2 reasoner. Journal of Automated Reasoning 53(3), 245{269 (2014)
13. Gomez-Romero, J., Bobillo, F., Ros, M., Molina-Solana, M., Ruiz, M.D., Mart nBautista, M.J.: A fuzzy extension of the semantic building information model. Automation in Construction 57, 202{212 (2015)
14. Horridge, M., Bechhofer, S.: The OWL API: A Java API for OWL ontologies. Semantic Web Journal 2(1), 11{21 (2011)
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)
16. Lisi, F.A., Straccia, U.: A logic-based computational method for the automated induction of fuzzy ontology axioms. Fundamenta Informaticae 124(4), 503{519 (2013)
17. Lisi, F.A., Straccia, U.: Learning in description logics with fuzzy concrete domains. Fundamenta Informaticae 140(3{4), 373{391 (2015)
18. Lloyd., S.P.: Least squares quantization in PCM. IEEE Transactions on Information Theory 28(2), 129{137 (1982)
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 identi cation of activities of daily living using mobile devices. Sensors 16(2), 184 (2016)
20. Staab, S., Studer, R. (eds.): Handbook on Ontologies. International Handbooks on Information Systems, Springer (2004)
21. Straccia, U.: Foundations of Fuzzy Logic and Semantic Web Languages. CRC Studies in Informatics Series, Chapman & Hall (2013)
22. Straccia, U., Mucci, M.: pFOIL-DL: Learning (fuzzy) 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). pp. 345{352. ACM, Salamanca, Spain (2015)
23. Turlach, B.A.: Bandwidth selection in kernel density estimation: A review. In: CORE and Institut de Statistique (1993)
24. W3C OWL Working Group: OWL 2 Web Ontology Language: Document Overview (2008), [Online] Available: http://www.w3.org/TR/owl2-overview
25. Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 338{353 (1965)
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BibTeX entry
@inproceedings{oai:it.cnr:prodotti:388978,
title = {Datil: learning fuzzy ontology datatypes},
author = {Huitzil I. and Straccia U. and Diaz-Rodriguez N. and Bobillo F.},
publisher = {Springer-Verlag, Berlin, DEU},
doi = {10.1007/978-3-319-91476-3_9},
booktitle = {IPMU 2018 - International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 100–112, Cádiz, Spain, 11-15 June, 2018},
year = {2018}
}CNR authorsLaboratories
0000-0001-5998-6757CNR ExploRA
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