An approach for Context-aware Service Discovery and Recommendation

1. An Approach for Context-aware Service Discovery and Recommendation service recommendation service discovery

2. Outline Introduction Our Approach Experiment Conclusion

3. Outline Introduction Our Approach Experiment Conclusion

4. Introduction Context type location, time Context Context value New York Context-aware system: react to a user’s context without their intervention

5. Problems Limited support for dynamic adaption to newly added context types Manually define all the context types Manually establish the relation between the sensed context scenario and the corresponding services in the form of if-then rules

6. Outline Introduction Our Approach Experiment Conclusion

7. Overview of our approach

8. Overview of our approach

9. Ontology Class: abstract description of a group of concepts with similar characteristics Individual: instance of a class Property: describes an attribute of class or individual Relation: ways classes or individuals associate with each other

10. Steps of find relevant ontologies Search with the context value YES NO Remove the first adj/adv, then search Annotated the ontology to the context YES NO String is empty Annotated the ontology to the context, convert the remove adj/adv to constraints Use synonyms of the context value

11. Overview of our approach

12. Identifing context relations Relations between two Context Values Intersection Complement Equivalence Independence

13. Identifing context relations Multiple Context Values: E-R model For each relation of two context values Convert the two context values into two entities in E-R model Convert the relation type into a relationship node

14. Steps of building integrated E-R model Filter out independence relations Remove equivalence relations Set the integrated E-R model as empty For each relation in the remainder relation list Convert the relation into an independent E-R model Add the independent E-R model to the integrated E-R model If exist similarity or equivalence entities, merge them by keeping the one with the richer information If exist subset or complement relations, add a relation ship node in the integrated E-R model If two relationship nodes contain the same relation type and relationship attributes, we merge them into one relationship node

15. Steps of building integrated E-R model Intersect Travel Los Angeles Tourist Attractions Integrated E-R model

16. Steps of building integrated E-R model Intersect Travel Los Angeles Intersect Los Angeles Lakers Tourist Attractions NBA Integrated E-R model

17. Steps of building integrated E-R model

18. Overview of our approach

19. Generating searching criteria Suppose are entities in the integrated E-R model. SharedElementsSetrepresents the set of a user’s needs.

20. Generating searching criteria Apply the rules on the E-R model Obtain a SharedElementSet Group the entities in SharedElementSet Each entity in SharedElementSet is treated as a group If the entities in one group are a subset of the entities in another group, we combine these two groups together. Repeat until no groups can be combined Extract keywords from each group as searching criteria

21. Outline Introduction Our Approach Experiment Conclusion

22. Experiment Objective Evaluation of the detected context relations Evaluation of Service Recommendation Precision，Recall

23. Evaluation of the detected context relations Five context scenarios Manually examine its context and identify the potential needs of the user Use our prototype to automatically find user’s needs

24. Evaluation of Service Recommendation Use the keywords in each group as searching criteria to search for online resources. Use Google and Seekda as the search engine to search for Web pages and Web services

25. Outline Introduction Our Approach Experiment Conclusion

26. Conclusion Use ontologies to enhance the meaning of a user’s context values The SharedElementSet reflects user’s needs Experiment is not clear..