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A Granular Computing Method for OWL Ontologies

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Warianty tytułu
Konferencja
Convegno Italiano di Logica Computazionale : CILC 2015 (30th; 01-03.07.2015; University of Genoa, Italy)
Języki publikacji
EN
Abstrakty
EN
We propose a method to extract and integrate fuzzy information granules from a populated OWL ontology. The purpose of this approach is to represent imprecise knowledge within an OWL ontology, as motivated by the fact that the Semantic Web is full of imprecise and uncertain information coming from perceptual data, incomplete data, data with errors, etc. In particular, we focus on Fuzzy Set Theory as a means for representing and processing information granules corresponding to imprecise concepts usually expressed by linguistic terms. The method applies to numerical data properties. The values of a property are first clustered to form a collection of fuzzy sets. Then, for each fuzzy set, the relative σ-count is computed and compared with a number of predefined fuzzy quantifiers, which are therefore used to define new assertions that are added to the original ontology. In this way, the extended ontology provides both a punctual view and a granular view of individuals w.r.t. the selected property. We use a real-world ontology concerning hotels and populated with data of the Italian city of Pisa, to illustrate the method and to test its implementation. We show that it is possible to extract granular properties that can be described in natural language and smoothly integrated in the original ontology by means of annotated assertions.
Słowa kluczowe
Wydawca
Rocznik
Strony
147--174
Opis fizyczny
Bibliogr. 36 poz., rys., tab., wykr.
Twórcy
autor
  • Dipartimento di Informatica, and C.I.L.A. (Centro Interdipartimentale di Logica e Applicazioni), Università degli Studi di Bari "Aldo Moro", Bari, Italy
autor
  • Dipartimento di Informatica, and C.I.L.A. (Centro Interdipartimentale di Logica e Applicazioni), Università degli Studi di Bari "Aldo Moro", Bari, Italy
Bibliografia
  • [1] Baader F, Calvanese D, McGuinness D, Nardi D, and Patel-Schneider P (eds.). The Description Logic Handbook: Theory, Implementation and Applications (2nd ed.). Cambridge University Press, 2007. ISBN:10:0521876257, ISBN: 13:9780521876254.
  • [2] Horrocks I, Kutz O, and Sattler U. The Even More Irresistible SROIQ. In: Doherty P, Mylopoulos J, Welty CA (eds.), Proceedings, Tenth International Conference on Principles of Knowledge Representation and Reasoning, Lake District of the United Kingdom, June 2-5, 2006. AAAI Press 2006 pp. 57-67. ISBN:978-1-57735-271-6.
  • [3] Stoilos G, Simou N, Stamou G, and Kollias S. Uncertainty and the Semantic Web. IEEE Intelligent Systems, 2006;21:84-87. doi:10.1109/MIS.2006.105.
  • [4] Zadeh LA. Is there a need for fuzzy logic? Information sciences, 2008;178(13):2751-2779. doi:10.1016/j.ins.2008.02.012.
  • [5] Alonso JM, Castiello C, and Mencar C. Interpretability of Fuzzy Systems: Current Research Trends and Prospects. In: Kacprzyk J, Pedrycz W (eds.), Springer Handbook of Computational Intelligence. Springer Berlin / Heidelberg. ISBN 978-3-662-43504-5, 2015.
  • [6] Zadeh LA. Fuzzy Sets. Information and Control, 1965. 8(3):338-353.
  • [7] Bargiela A, and Pedrycz W. Human-centric information processing through granular modelling, volume 182. Springer Science & Business Media, 2009. ISBN: 978-3-540-92915-4, 978-3-642-10092-5.
  • [8] Straccia U. Foundations of Fuzzy Logic and Semantic Web Languages. CRC Studies in Informatics Series. Chapman & Hall, 2013. ISBN:1439853479, 9781439853474.
  • [9] Bargiela A, and Pedrycz W. Granular computing: an introduction. Springer Science & Business Media 717, 2003. ISBN: 978-1-4020-7273-4, 978-1-4613-5361-4.
  • [10] Delgado M, Sánchez D, and Vila MA. Fuzzy cardinality based evaluation of quantified sentences. International Journal of Approximate Reasoning, 2000;23(1):23-66. doi:10.1016/S0888-613X(99)00031-6.
  • [11] Zadeh LA. A computational approach to fuzzy quantifiers in natural languages. Computers & Mathematics with Applications, 1983;9(1):149-184. doi:10.1016/0898-1221(83)90013-5.
  • [12] Zimmermann HJ. Fuzzy set theory. Wiley Interdisciplinary Reviews: Computational Statistics, 2010;2(3):317-332. doi:10.1002/wics.82. URL http://doi.wiley.com/10.1002/wics.82.
  • [13] Zadeh L. From computing with numbers to computing with words. From manipulation of measurements to manipulation of perceptions. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 1999;46(1):105-119. doi:10.1109/81.739259. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=739259.
  • [14] Trillas E, Termini S, and Moraga C. A naïve way of looking at fuzzy sets. Fuzzy Sets and Systems, 2016;292:380-395. doi:10.1016/j.fss.2014.07.016. URL http://linkinghub.elsevier.com/retrieve/pii/S0165011414003376.
  • [15] Toth H. Fuzziness: From Epistemic Considerations to Terminological Clarification. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1997;05(04):481-503. doi:10.1142/S021848859700035X. URL http://www.worldscientific.com/doi/abs/10.1142/S021848859700035X.
  • [16] Zadeh LA. The Information Principle. Information Sciences, 2015;294:540-549. doi:10.1016/j.ins.2014.09.026.
  • [17] de Oliveira J. Semantic constraints for membership function optimization. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 1999;29(1):128-138. doi:10.1109/3468.736369. URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=736369.
  • [18] Saaty T, and Ozdemir M. Why the magic number seven plus or minus two. Mathematical and Computer Modelling, 2003;38(3):233-244. doi:10.1016/S0895-7177(03)90083-5.
  • [19] Bezdek JC. Fuzzy Clustering. In: Ruspini EH, Bonissone PP, Pedrycz W (eds.), Handbook of Fuzzy Computation, p. 2. Institute of Physics Pub., 1998.
  • [20] Dubois D, and Prade H. Fuzzy cardinality and the modeling of imprecise quantification. Fuzzy sets and Systems, 1985;16(3):199-230. doi:10.1016/0165-0114(85)90025-9.
  • [21] Liu Y, and Kerre EE. An overview of fuzzy quantifiers. (I). Interpretations. Fuzzy Sets and Systems, 1998;95(1):1-21. doi:10.1016/S0165-0114(97)00254-6.
  • [22] Delgado M, Ruiz MD, Sánchez D, and Vila MA. Fuzzy quantification: a state of the art. Fuzzy Sets and Systems, 2014;242:1-30. doi:10.1016/j.fss.2013.10.012.
  • [23] Schmidt-Schauss M, and Smolka G. Attributive Concept Descriptions with Complements. Artificial Intelligence, 1991;48(1):1-26. URL https://doi.org/10.1016/0004-3702(91)90078-X.
  • [24] Borgida A. On the Relative Expressiveness of Description Logics and Predicate Logics. Artificial Intelligence, 1996;82(1-2):353-367. URL https://doi.org/10.1016/0004-3702(96)00004-5
  • [25] Reiter R. Equality and Domain Closure in First Order Databases. Journal of ACM, 1980;27:235-249. doi:10.1145/322186.322189.
  • [26] Baader F, and Hanschke P. A Scheme for Integrating Concrete Domains into Concept Languages. In: Mylopoulos J, Reiter R (eds.), Proceedings of the 12th International Joint Conference on Artificial Intelligence. Sydney, Australia, August 24-30, 1991. Morgan Kaufmann 1991 pp. 452-457. ISBN: 1-55860-160-0.
  • [27] Horrocks I, Patel-Schneider PF, and van Harmelen F. From SHIQ and RDF to OWL: The Making of a Web Ontology Language. Journal of Web Semantics, 2003;1(1):7-26. doi:10.1016/j.websem.2003.07.001.
  • [28] Straccia U. Description Logics with Fuzzy Concrete Domains. In: UAI ’05, Proceedings of the 21st Conference in Uncertainty in Artificial Intelligence, Edinburgh, Scotland, July 26-29, 2005. AUAI Press 2005 pp. 559-567. ISBN 0-9749039-1-4.
  • [29] Straccia U. Reasoning within Fuzzy Description Logics. Journal of Artificial Intelligence Research, 2001;14:137-166. doi:10.1613/jair.813.
  • [30] Sanchez D, and Tettamanzi AG. Fuzzy quantification in fuzzy description logics. In: Sanchez E (ed.), Fuzzy Logic and the Semantic Web, volume 1 of Capturing Intelligence. Elsevier, 2006 pp. 135-159. URL http://www.sciencedirect.com/science/article/pii/S1574957606800109.
  • [31] Bobillo F, and Straccia U. fuzzy DL: An expressive fuzzy description logic reasoner. In: FUZZ-IEEE 2008, IEEE International Conference on Fuzzy Systems, Hong Kong, China, 1-6 June, 2008, Proceedings. IEEE 2008 pp. 923-930. ISBN: 978-1-4244-1818-3.
  • [32] Bobillo F, and Straccia U. The fuzzy ontology reasoner fuzzy DL. Knowledge-Based Systems, 2016;95:12-34. URL https://doi.org/10.1016/j.knosys.2015.11.017.
  • [33] Bobillo F, and Straccia U. Representing fuzzy ontologies in OWL 2. In: FUZZ-IEEE 2010, IEEE International Conference on Fuzzy Systems, Barcelona, Spain, 18-23 July, 2010, Proceedings. IEEE, 2010 pp.1-6. doi:10.1109/FUZZY.2010.5584661.
  • [34] Bobillo F, Straccia U. Fuzzy ontology representation using OWL 2. International Journal of Approximate Reasoning, 2011;52(7):1073-1094. URL https://doi.org/10.1016/j.ijar.2011.05.003.
  • [35] Lisi FA, Mencar C. Towards fuzzy granulation in OWL ontologies. In: Ancona D, Maratea M, Mascardi V (eds.), Proceedings of the 30th Italian Conference on Computational Logic, Genova, Italy, July 1-3, 2015., volume 1459 of CEUR Workshop Proceedings. CEUR-WS.org, 2015 pp. 144-158. URL http://ceur-ws.org/Vol-1459/paper19.pdf.
  • [36] Lisi FA, Straccia U. Learning in Description Logics with Fuzzy Concrete Domains. Fundamenta Informaticae, 2015;140(3-4):373-391. doi:10.3233/FI-2015-1259. URL http://dx.doi.org/10.3233/FI-2015-1259.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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