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Modeling and Reasoning about Uncertainty in Context-Aware Systems

Modeling and Reasoning about Uncertainty in Context-Aware Systems. Binh An Truong, Young-Koo Lee and Sung-Young Lee IEEE International Conference on e-Business Engineering, 2005. Outline. Introduction Related work A scenario framework Unified Context Ontology Ontology language: PROWL

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Modeling and Reasoning about Uncertainty in Context-Aware Systems

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  1. Modeling and Reasoning about Uncertainty in Context-Aware Systems Binh An Truong, Young-Koo Lee and Sung-Young Lee IEEE International Conference on e-Business Engineering, 2005

  2. Outline • Introduction • Related work • A scenario framework • Unified Context Ontology • Ontology language: PROWL • Reasoning about context • Conclusions

  3. Introduction • Context might be considered as a collection of information which characterizes the interaction between a user and the application. • Uncertainty is an unavoidable factor in any context-aware system. • It is mostly caused by the imperfectness and incompleteness of sensed data.

  4. Bayesian Networks • The Bayesian networks method, which is used commonly in AI community, is a very powerful method for the representation and reasoning about the uncertainty. • A Bayesian network is a representation of a full joint distribution over a set of random variables. • Nevertheless, a fundamental limitation of using Bayesian network for knowledge representation is that it can not represent the structural and relational information.

  5. Bayesian Networks • 貝式網路又稱為信念網路(Belief Network)透過資料結構表現變數之間的相依性，並透過對聯集機率分配做為呈現。 • 特性： • 透過隨機變數的集合構成該網路的各個節點 • 一個有向連結或箭號的集合連接成的節點，一個由節點X到節點Y的箭號代表X對Y有直接影響 • 每個節點有一個條件機率表(Conditional Probability Table, CPT)可記錄父節點對子節點的定量影響。 • 圖中不存在有向循環(或稱為有向非循環圖)

  6. Bayesian Networks Fig1. Bayesian Networks

  7. Probabilistic Model • Authors use probability for representing the uncertainty within a domain. • A class which consists of probabilistic information is annotated with the local probabilistic model that is called p-class. • A p-class, similarly to the normal class, has properties, relations and restrictions.

  8. Probabilistic Model • P-class has a property that is either simple or complex. • Simple property: • With a root node in Bayesian network • Has two restrictions: hasValue and hasPD • Complex property: • With a node in Bayesian network which has a set of parent nodes • Has two restrictions: hasParent and hasCPT

  9. Scenario Fig2. A relational schema of scenario

  10. Ontological Reasoning • 基本邏輯最主要探討核心為：論證，論證好壞將影響整體邏輯之順暢度與正確性與否。 • 每項論證均包含了前提(代表符號：P1, P2)與結論(代表符號：Q)，而前提與結論表達順序分為：(1) P1, P2, Q、(2) P1, Q, P2,與(3) Q , P1, P2三種型式 • 論證主要分為：歸納論證(Inductive Argument)與演繹論證(Deductive Argument)

  11. Unified Context Ontology Fig3. A two-layer context ontology for relational and probabilistic knowledge

  12. Ontology language: PROWL • RelationChain主要是用來連結2個類別 <RelationChain rdf:ID=”RC-ofWindow.ofRoom”> <hasRelationChain rdf:datatype=”&xsd;string”> ofWindow.ofRoom </hasRelationChain> </RelationChain>

  13. Ontology language: PROWL • PropertyChain主要呈現關聯鏈，並且定義其資料型態 <PropertyChain rdf:ID=”PC-ofWindow.ofRoom.hasLinghtStatus”> <hasRelationChain rdf:resource=”#RC-ofWindow.ofRoom”/> <hasProperty rdf:datatype=”&xsd;string”> hasLightStatus </hasProperty> </PropertyChain>

  14. Ontology language: PROWL • OWL language does not support to describe new concepts that authors use in our context model. Thus, authors introduce three new additional elements: rdf:hasDist, rdf:hasParents and rdf:hasCPT. <rdf:Property ref:ID=”hasDist”> <rdfs:label>hsaDist</rdfs:label> <rdfs:domain rdfsource=”#Restriction”/> <rdfs:range rdf:resource=”&rdf;List”/> </rdf:Property>

  15. Ontology language: PROWL

  16. Ontology language: PROWL

  17. Reasoning about context Fig4. An example of the context ontology for the scenario

  18. Conclusions • This paper describes author’s approach of representing and reasoning about uncertain context. • This paper shows that the proposed context model is feasible and necessary for supporting context modeling and reasoning in pervasive computing. • Authors are exploring method to integrate multiple reasoning methods from AI area and supported representation mechanism into the context reasoning and management layer.

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