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Fuzzy OWL-2 Annotation for MetOcean Ontology

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  1. 1. Universiti Teknologi PETRONASDepartment of Computer & Information Sciences Seri Iskandar, 31750 Tronoh, Perak, MalaysiaFuzzy OWL-2 Annotation for MetOcean Ontology International Symposium on Agricultural Ontology Service 2012 (AOS2012)3 to 4 September 2012 Authors: K. U. Danyaro, J. Jaafar, and M. S. Liew
  2. 2. OutlineMotivationsIntroduction What? Description Logic OWL 2Fuzzy OWL 2 AnnotationHow? MetOceanDiscussionQA
  3. 3. Motivations Description logic (DL) is family of formal knowledge representationlanguage that has expressive power in reasoning concepts [1]. Provides logical formalism for ontologies and the Semantic Web Needs fuzzy representation in order to meet the real world ontologysystem. Fuzzy DL are presented by extending classic DL to support the imprecise information processing in ontology systems. OWL needs to be used for representing the knowledge of a specific concept. Meteorological and oceanographic (MetOcean) environment is also anappropriate place to represent the knowledge based on fuzzy ontologies.
  4. 4. OutlineMotivationsIntroduction What? Description Logic OWL 2Fuzzy OWL 2 Annotation MetOceanDiscussionQA
  5. 5. Introduction Description Logic (DL) A fragment of First-Order Logic (FOL). Tarski-style declarative Semantics that enable capturing the standard knowledge representation[2]. Is standardized by W3C standard for OWL Semantic Web (currently OWL 2) as the KR formalism. Logic Ontology Computation
  6. 6. Introduction An ontology is a formal explicit specification of a shared conceptualizationof a domain of interest [3, 4]. Description logic is usually employed to represent the knowledge and logicof an ontology. OWL helps in making connections between human and machine throughthe logic concepts Latest standardized OWL is OWL-2 which has a good feature for interaction betweenmachine and human i.e. Annotation. OWL is a computational logic-based language such that knowledge expressed in OWL canbe reasoned with by computer programs either to verify the consistency of that knowledgeor to make implicit knowledge explicit[5]. OWL 2 has three important properties: object property, datatype property, and annotation property.
  7. 7. OutlineMotivationsIntroduction Description Logic OWL 2Fuzzy OWL 2 Annotation How? MetOceanDiscussionQA
  8. 8. MetOcean Dataset MetOcean in situ contains large amount of data supplied by CerigaliPETRONAS Sdn Bhd which is an Asia branch of MetOcean Company. The 104.2e longitude and 5.45n latitude of Kota Kinabalu, a Malaysianregion of MetOcean has been used. The time series data for the 2005 year, ranges from 1st January 2005 00:00to 31st December 2005 23:00 was extracted using OSMOSIS software. The typical hindcast data have been resulted in an array format. The data spanned based on: YYYYMM, DDHH, WD, WS, ETOT, TP, VMD,ETOT1, TP1, VMD1, ETOT2, TP2, VMD2 and HSIG. Set all the datasets in form of OWL-2 relationships and particularized onfuzzy variables (fuzzy elements for uncertainty and imprecision of the data).
  9. 9. OSMOSIS.
  10. 10. Fuzzy OWL 2 AnnotationFuzzy Interpretation The convention of a statement in fuzzy logic is either true or false, 0 or1. the degree of truth of a statement is in the interpretation I. fuzzy statement can be within f [0, 1], f or f , is a statement Definition: Let x be an element of I (Interpretation domain) and .I bethe fuzzy interpretation function then the fuzzy interpretation I is a pair,I = (I, .I) such that for every individual x mapped onto an element xI of I, for every concept C mapped onto CI : I [0, 1], for every role R mapped onto a function RI: I I [0, 1]. Fuzzy interpretation I maps each statement into [0, 1], i.e. I [0, 1]
  11. 11. Fuzzy OWL 2 AnnotationExampleWind Direction (deg) Implies that the interpretation of very high can be determined by f I: ID [0, 1]. Where D isa datatype property with ; ID is the interpretation domain and the set of fuzzypredicate. Entailment equation as [f1 , f2] Trapezoidal (f1 , f2, a , b, c, d), triangular (f1 , f2, a , b, c), right (f1 , f2, a , b) and left (f1 , f2, a , b). f1 = 0, f2 = 1 is the modified datatype.
  12. 12. Fuzzy OWL 2 AnnotationExampleDatatype HighWindDirection: [0, 250] [0, 1] represents the degree to which Wind is being high to theNorth asHighWindDirection(x) = trapezoidal (0, 250, 18, 50, 62, 70= triangular (0, 250, 18, 50, 62)= right (0, 250, 18, 50)= left (0, 250, 18, 50)
  13. 13. Fuzzy OWL 2 Annotation(a) Left-shoulder function (b) Right-shoulder function(C) Triangular function (d) Trapezoidal function
  14. 14. Fuzzy OWL 2 Annotation Applying the definition then the concept C mapped CI: I [0, 1]. Implies CI is satisfiable because x I, CI(x) > 0 C can be considered as satisfiable since in KB, I determines the maximum degree oftruth that the concept C may have over all individuals, x I.
  15. 15. Fuzzy OWL 2 AnnotationFuzzy Annotation propertydefining Kinabalus fuzzy OWL concepts
  16. 16. Fuzzy OWL 2 AnnotationFuzzy Annotation propertyFuzzy annotation for the speed of wind direction.
  17. 17. Conclusion The expressiveness of fuzzy OWL 2 knowledge has been achieved based on languagerepresentation using meteorological data. The proposed method suggests the use of fuzzy OWL 2 for solving the problem of uncertainty inmeteorological data. The presence of fuzzy OWL 2 power will reduce the ambiguity of information in the knowledgebase. Finally suggests the use of ontology editor and reasoners in providing the error-free annotation.
  18. 18. Reference[1] F. Bader et al. (editors): The description Logic Handbook (Theory, Implementation and Applications), Cambridge University Press, 2003.[2] Bobilloa, F., Straccia, U.: Fuzzy Description Logics with General t-norms and Data Types, Fuzzy Sets and Systems, vol. 160, pp.33823402 (2009).[3] C. A. Yeung and H. Leung, "A formal model of ontology for handling fuzzy membership and typicality of instances", The computer journal, vol. 53, No. 3, 2010.[4] Horrocks, I, Glimm, B., Sattler, U.: Hybrid Logics and Ontology Languages, Electronic Notes in theoretical Computer Science, vol. 174, pp. 314 (2007).[5][7] Bobillo, F., Straccia, U.: Fuzzy Ontology Representation Using OWL 2, International Journal of Approximate Reasoning, vol. 52, 1073-1094 (2011)[8] Russell, S. J., Norvig, P.: Artificial Intelligence: A Modern Approach (2nd eds) Pearson Education, New Jersey (2010)[9] Fuzzy ontology plug-in (fuzzyDL 1.1). Available at:
  19. 19. Universiti Teknologi PETRONASDepartment of Computer & Information Sciences Seri Iskandar, 31750 Tronoh, Perak, MalaysiaThank you!
  20. 20. Fuzzy OWL 2 Annotation
  21. 21. Fuzzy OWL 2 Annotation

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