1. Semantic Context Specification April 28, 2004 Amit Nanda, Reiman Rabbani, Maciej Janik

2. Introduction Context – What is it? Semantic context. Benefits from having a context ?

3. Example – Hotel check-in David –business trip –has to get up at 7am –does not like AC –has to watch CNN finance news –teetotaller Robert –leisure trip –can sleep till 9am –likes to have cool room –watches Sports –likes to drink champaign

4. default room settings Example – Hotel check-in alarm set at 7am AC turned off TV starts with CNN non-alcoholic drinks in fridge default room settings alarm set at 9am AC turned to 70 F TV starts with Sport channel champaign in fridge default room settings David Robert

5. Context = f ( task, ontology, data ) The context circle TASK Execution context User Application Web Application Additional information Additional information User Context WebAp p Context WebAp p Context Task Context Our Proposed Model

6. Context: TASK Operation that has to be performed. Goal to accomplish. Assumption : Task has to be application specific for best results. Example : Task = travel. On expedia.com and on amazon.com, results differ.

7. Context: DATA Vast store of information, covering different domains, a part of which constitutes the end result for user task. In semantic web, all data corresponds to real world objects and objects have different types of relations between each other Denoted in the form of triples, encoded as graphs in RDF files.

8. Context: ONTOLOGY “Specification of a conceptualization.”* Can be visualized as skeleton or framework for the data. Specified by OWL or DAML-OIL. Assumption : a task that needs to be executed, should have a well defined ontology. *Gruber, T.R. A Translation Approach to Portable Ontology Specifications. 1993.

9. Profile Ontology Dynamic Profile Update ‘Profile’ as an ontology Assumption: Everything in the ontology is a universally defined object When new object are required the ontology and relationships of that object can be downloaded User profile may or may not be updated. If task is executed using the user’s context, profile is updated based on rules & settings. If user’s profile has no information regarding task and if it’s still executed, profile is updated using current results. Profile is not updated if task is not executed

10. Profile Ontology

11. <rdf:RDF xmlns="http://a.com/ontology#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax- ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:owl="http://www.w3.org/2002/07/owl#" xml:base="http://a.com/ontology">

12. Task Ontology Inventory DB Ontology Knowledge Base Task Application User Application Other Stored/ Inferred Data Rules Temporal Spatial Static Abstract Private … Profile Ontology Task Answers {….} Questions {….} Profile Data Context sensitive results

13. Camera Ontology

14. Camera Ontology - sample.... 550 500 450.

15. P R O F I L E Knowledge Base Example of camera context... 550 500 450 550 500 450 User AppMerchant App Camera Ontology Inventory DB buy camera cameras to offer personalized view profile query: what cameras do you have ? my cameras... what is a „camera” ? Context

16. Security Issues Greater the details provided by user application from user’s profile, better the result. But the user application is required not to provide user details regarding salary, SSN, bank account etc This is a constraint or drawback.

17. Conclusions In our model, context is created on application side, using information provided by user or profile. Specialized application is able to create a better context, than general user application.

18. Conclusions Because of the vastness of application areas, difficult to categorize them into definite types. Generalization of context is difficult and not always feasible. It is not possible to create a unified context that suits every need.

19. Conclusions Profile may grow to enormous proportions (including vast amount of information) and become hard to manage. Security issues may arise, because of complexity of data. Excessive transparency of profile will lead to abuse by malicious applications Sensitive information can be inferred and rules can be bypassed by use of circular logic by dishonest programmers Future work: apply this model of context to different applications.

20. Questions? Thank you !