Predictive and Contextual Feature Separation for Bayesian Metanetworks

1. Predictive and Contextual Feature Separation for Bayesian Metanetworks Vagan Terziyan vagan@cc.jyu.fi Industrial Ontologies Group, University of Jyväskylä, Finland KES-2007, Vietri sul Mare , Italy 12 September 2007 Session IS03: Context-Aware Adaptable Systems and Their Applications (17:10, room D)

2. Contents • Bayesian Metanetworks • Metanetworks for managing conditional dependencies • Metanetworks for managing feature relevance • Feature Separation for Bayesian Metanetworks • Conclusions Vagan Terziyan Industrial Ontologies Group Department of Mathematical Information Technologies University of Jyvaskyla (Finland) http://www.cs.jyu.fi/ai/vagan This presentation: http://www.cs.jyu.fi/ai/KES-2007.ppt

3. Conditional dependence between variables X and Y Random variable X {x1, x2, …, xn} Fixed conditional probability table: Random variable Y {y1, y2, …, ym} P(Y) = X (P(X) · P(Y|X))

4. Bayesian Metanetwork • Definition.The Bayesian Metanetwork is a set of Bayesian networks, which are put on each other in such a way that the elements (nodes or conditional dependencies) of every previous probabilistic network depend on the local probability distributions associated with the nodes of the next level network.

5. Two-levelBayesian C-Metanetworkfor Managing Conditional Dependencies

6. Two-level Bayesian C-Metanetwork for managing conditional dependencies

7. Two-level Bayesian R-Metanetworkfor Modelling Relevant Features’ Selection

8. Feature relevance modelling We consider relevance as a probability of importance of the variable to the inference of target attribute in the given context. In such definition relevance inherits all properties of a probability.

9. General Case of Managing Relevance Probability P(XN)

10. Example of Relevance Bayesian Metanetwork Conditional relevance !!!

11. Example of Relevance Bayesian Metanetwork

12. Separation of contextual and predictive attributes is based on: • Part_ofcontext • Role-based context • Interface-based context

13. The nature of part_of context air pressure dust humidity temperature Machine emission Environment Sensors X x5 x6 x7 x2 x3 x4 x1 contextual attributes predictive attributes

14. Context Description Framework (CDF) Basic Data Model Khriyenko O., Terziyan V., A Framework for Context-Sensitive Metadata Description, In: International Journal of Metadata, Semantics and Ontologies,Inderscience Publishers,ISSN 1744-2621, 2006, Vol. 1, No. 2, pp. 154-164.

15. Part-of Context in CDF part_of Resource i Property_q Value_m Property_n Property_p Resource k Value_r Value_s RDF container Context_h RDF statement Resource_i Property_q Value_m true_in_context Resource_k Property_n Value_r Resource_i Property_p Value_s Predictive feature Contextual features

16. Multiple Context Inheritance … Golf_Club part_of located_in Paris members_amount 36 has_age John 48 y. located_in Bagnolet belongs_to Symphonic_Orchestra State part_of

17. Role-based context Team Member Concursant The example of the proactive object (human resource), which is member of several organization and which is playing different roles in each of them. The context of this object should include the description of these roles (duties, commitments, responsibilities, etc). Human Resource Wife Manager

18. Interface-based context b a The example of the domain object (aircraft) is shown in different interfaces: (a) Google Maps; (b) pilots’ control panel; (c) manufacturing design e-manual. Each interface is considered as a context, which affect on which parameters of the aircraft are to be shown c

19. Summary • We are considering a context as a set of contextual attributes, which are not directly effect probability distribution of the target attributes, but they effect on a “relevance” of the predictive attributes towards target attributes. • Bayesian Metanetwork allows modelling such context-sensitive feature relevance. The model assumes that the relevance of predictive attributes in a Bayesian network might be a random attribute itself and it provides a tool to reason based not only on probabilities of predictive attributes but also on their relevancies. • For Bayesian Metanetwork there is a need to distinguish predictive and contextual attributes and in this paper the separation of attributes is described based on three notions of a context: part_of context, role-based context and interface-based context.

20. Read more about Bayesian Metanetworks in: Terziyan V., A Bayesian Metanetwork, In:International Journal on Artificial Intelligence Tools, Vol. 14, No. 3, 2005, World Scientific, pp. 371-384. http://www.cs.jyu.fi/ai/papers/IJAIT-2005.pdf Terziyan V., Vitko O., Bayesian Metanetwork for Modelling User Preferences in Mobile Environment, In: German Conference on Artificial Intelligence (KI-2003), LNAI, Vol. 2821, 2003, pp.370-384. http://www.cs.jyu.fi/ai/papers/KI-2003.pdf Terziyan V., Vitko O., Learning Bayesian Metanetworks from Data with Multilevel Uncertainty, In: M. Bramer and V. Devedzic (eds.), Proceedings of the First International Conference on Artificial Intelligence and Innovations, Toulouse, France, August 22-27, 2004, Kluwer Academic Publishers, pp. 187-196 . http://www.cs.jyu.fi/ai/papers/AIAI-2004.ps