TURAMBAR: An Approach to Deal with Uncertainty in Semantic Environments
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, 2012TURAMBAR 1/11
Outline Introduction to OWL OWL limitations TURAMBAR Approach TURAMBAR Example Conclusions and Future WorksTURAMBAR 2/11
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 ReasonersTURAMBAR ► HermiT Introducition to OWL 3/11
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 – DeLoreanTURAMBAR OWL Limitations 4/11
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 OWLAPITURAMBAR TURAMBAR Approach 5/11
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
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
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
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
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, 2012TURAMBAR 10/11
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