1. TURAMBAR: An Approach to Deal with
Uncertainty in Semantic Environments
IWAAL 2012
David Ausín, Federico Castanedo and Diego López -de-Ipiña
DeustoTech - Deusto Institute of Technology, University of Deusto
http://www.morelab.deusto.es
December 4, 2012
TURAMBAR 1/11

2. Outline
 Introduction to OWL
 OWL limitations
 TURAMBAR Approach
 TURAMBAR Example
 Conclusions and Future Works
TURAMBAR 2/11

3. Introducition to OWL
► OWL Web Ontology Language created by W3C
► Last version OWL 2
► The meaning of an ontology is assigned by the
formal semantics:
► RDF-Based Semantics
► Direct Semantics → Description Logics
► OWL 2 provides profiles: OWL 2 EL, OWL 2 QL and
OWL 2 RL
► OWL 2 axioms are divided in ABox, TBox and
Rbox.
► Open world assumption
► OWL Reasoners
TURAMBAR
► HermiT Introducition to OWL 3/11

4. OWL Limitations
► Management of uncertainty and vagueness
► Uncertainty:
something is true or false but we do
not have enough information to ensure it
► Possibilistic theory
– PossDL
► Probabilistic theory
– Pronto
– BayesOWL
► Vagueness: something is true to certain grade
► Fuzzylogic
– FuzzyDL
– DeLorean
TURAMBAR OWL Limitations 4/11

5. TURAMBAR Approach
► Goal: determine the probability that a fact
were true via the relationships and influences
that other facts have on this.
► How to achieve it?
► Combine Bayesian Networks with OWL 2 reasoning
► Bayesian nodes and edges are described using
OWL 2 annotations.
► Other features:
► SWRL Built-ins
► Probability learning from historical data
► Extension of Pellet and OWLAPI
TURAMBAR TURAMBAR Approach 5/11

6. TURAMBAR Example I
Annotat ionAs s e r t ion ► Defines a node
( p0 : named Location for
taismanPropertyProbability
p0 : atLocat ion
an object property
"ID: Location ► The values of
class(p0 .Kitchen ): 0 . 3 3 ; location may be a
class(p0 .Bedroom ): 0 . 4 1 ; child of a class with
class(p0 .LivingRoom ): 0 . 1 6 6 ; a probability.
class(p0 .Bathroom ): 0 . 0 8 3 ;
")
TURAMBAR TURAMBAR Example 6/11

7. TURAMBAR Example II
AnnotationAssertion ( ► Defines a Bayesian
p0 : node for a datatype
talismanPropertyProbability
p0 : time "ID: Time
property named
>= 0 && <= 28800 : 0 . 3 3 3 ; Time
>= 28800 && <=57600 : 0 . 3 3 3 ; ► It defines the
>= 57600 && <= 86400 : 0 . 3 3 3 ; " probability of
)
having a value in a
range.
TURAMBAR TURAMBAR Example 7/11

8. TURAMBAR Example III
AnnotationAs sert ion ( p0 : t a ► Defines a node
lismanClassProbabi l i t y
p0 : Eat ngAct ion named EatingAction
"ID: EatigAct ion and its dependencies
InitGraph
Time -> t h i s ;
Locat ion -> t h i s ;
EndGraph
p0 . Br eakfas tAc t ion :
0.125,0.1,0,0,0.9,0.9,0.6
,0,0,0,0,0;
p0 . LunchAction :
0,0,0,0,0,0,0,0,0.27,0.2
,0.1,0;
p0 . DinnerAct ion :
0.125,0.1,0,0,0,0,0,0,0.2
7,0.2,0.1,0;
p0 . SnackingAct ion :
TURAMBAR TURAMBAR Example 8/11

9. Conclusions and Future Works
► We present a probabilistic approach to handle
uncertainty in AmI environments. The presented
approach can be generalized to other semantic
environments.
► TURAMBAR tackles uncertainty by combining
Bayesian Networks with OWL reasoning.
► It will provide an extension of a well known
API for developers.
TURAMBAR Conclusions and Future Works 9/11

10. Thanks for your attention
David Ausín, Federico Castanedo and Diego López –de-Ipiña
DeustoTech - Deusto Institute of Technology, University of Deusto
http://www.morelab.deusto.es
December 4, 2012
TURAMBAR 10/11