Download pdf - Fuzzy OWL-2 Annotation for MetOcean Ontology

Authors: K. U. Danyaro, J. Jaafar, and M. S. Liew

Fuzzy OWL-2 Annotation for MetOcean

Ontology

International Symposium on Agricultural Ontology

Service 2012 (AOS2012) 3 to 4 September 2012

Universiti Teknologi PETRONAS Department of Computer & Information Sciences

Seri Iskandar, 31750 Tronoh, Perak, Malaysia

Outline

Motivations

Introduction

Description Logic

OWL 2

Fuzzy OWL 2 Annotation

MetOcean

Discussion

QA

How?

What?

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.

Outline

What?

Motivations

Introduction

Description Logic

OWL 2


MetOcean

Discussion

QA

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

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.

Outline

How?

Motivations

Introduction

Description Logic

OWL 2


MetOcean

Discussion

QA

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).

OSMOSIS .


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]

Fuzzy Interpretation

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>; ∆I

D 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.


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)

Example


(a) Left-shoulder function (b) Right-shoulder function

(C) Triangular function (d) Trapezoidal function


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.


defining Kinabalu’s fuzzy OWL concepts

Fuzzy Annotation property


Fuzzy annotation for the speed of wind direction.

Fuzzy Annotation property

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.

[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

Reference

Thank you!

Universiti Teknologi PETRONAS Department of Computer & Information Sciences

Seri Iskandar, 31750 Tronoh, Perak, Malaysia

