Presentation held by K.U. Danyaro, J. Jaafar, and M.S. Liew at the Agricultural Ontology Service (AOS) Workshop 2012 in Kutching, Sarawak, Malaysia from September 3 - 4, 2012

1. Universiti Teknologi PETRONAS
Department of Computer & Information Sciences
Seri Iskandar, 31750 Tronoh, Perak, Malaysia
Fuzzy 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. Outline
Motivations
Introduction What?
Description Logic
OWL 2
Fuzzy OWL 2 Annotation How?
MetOcean
Discussion
QA

3. Motivations
 Description logic (DL) is family of formal knowledge representation
language 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 ontology
system.
 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 an
appropriate place to represent the knowledge based on fuzzy ontologies.

4. Outline
Motivations
Introduction What?
Description Logic
OWL 2
Fuzzy OWL 2 Annotation
MetOcean
Discussion
QA

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. Introduction
 An ontology is a formal explicit specification of a shared conceptualization
of a domain of interest [3, 4].
 Description logic is usually employed to represent the knowledge and logic
of an ontology.
 OWL helps in making connections between human and machine through
the logic concepts
 Latest standardized OWL is OWL-2 which has a good feature for interaction between
machine and human i.e. Annotation.
 “OWL is a computational logic-based language such that knowledge expressed in OWL can
be reasoned with by computer programs either to verify the consistency of that knowledge
or to make implicit knowledge explicit”[5].
 OWL 2 has three important properties: object property, datatype property, and annotation
property.

7. Outline
Motivations
Introduction
Description Logic
OWL 2
Fuzzy OWL 2 Annotation How?
MetOcean
Discussion
QA

8. MetOcean Dataset
 MetOcean in situ contains large amount of data supplied by Cerigali
PETRONAS Sdn Bhd which is an Asia branch of MetOcean Company.
 The 104.2e longitude and 5.45n latitude of Kota Kinabalu, a Malaysian
region of MetOcean has been used.
 The time series data for the 2005 year, ranges from 1st January 2005 00:00
to 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 on
fuzzy variables (fuzzy elements for uncertainty and imprecision of the data).

9. OSMOSIS
.

10. Fuzzy OWL 2 Annotation
Fuzzy Interpretation
 The convention of a statement in fuzzy logic is either true or false, 0 or
1.
 ⇒ the degree of truth of a statement ϕ is in the interpretation I.
 ⇒ fuzzy statement can be within f ∈ [0, 1], ϕ ≥ f or ϕ ≤ f , ϕ is a
statement
 Definition: Let x be an element of ∆I (Interpretation domain) and .I be
the 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. Fuzzy OWL 2 Annotation
Example
Wind Direction (deg)
 Implies that the interpretation of very high can be determined by f I: ∆ID → [0, 1]. Where D is
a datatype property with <∆ID , 𝛗D>; ∆ID is the interpretation domain and the set of fuzzy
predicate.
 Entailment equation as [f1 , f2] ⊆ Τ
 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. Fuzzy OWL 2 Annotation
Example
Datatype HighWindDirection: [0, 250] ⟶ [0, 1] represents the degree to which Wind is being high to the
North as
HighWindDirection(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. Fuzzy OWL 2 Annotation
(a) Left-shoulder function (b) Right-shoulder function
(C) Triangular function (d) Trapezoidal function

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 of
truth that the concept C may have over all individuals, x ∈ ∆I.

15. Fuzzy OWL 2 Annotation
Fuzzy Annotation property
defining Kinabalu’s fuzzy OWL concepts

16. Fuzzy OWL 2 Annotation
Fuzzy Annotation property
Fuzzy annotation for the speed of wind direction.

17. Conclusion
 The expressiveness of fuzzy OWL 2 knowledge has been achieved based on language
representation using meteorological data.
 The proposed method suggests the use of fuzzy OWL 2 for solving the problem of uncertainty in
meteorological data.
 The presence of fuzzy OWL 2 power will reduce the ambiguity of information in the knowledge
base.
 Finally suggests the use of ontology editor and reasoners in providing the error-free annotation.

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.3382–3402 (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. 3—14 (2007).
[5] http://www.w3.org/2007/OWL/wiki/Primer
[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: http://nemis.isti.cnr.it/~straccia/software/fuzzyDL/fuzzyDL.html

19. Universiti Teknologi PETRONAS
Department of Computer & Information Sciences
Seri Iskandar, 31750 Tronoh, Perak, Malaysia
Thank you!

20. Fuzzy OWL 2 Annotation

21. Fuzzy OWL 2 Annotation