An approach for Context-aware Service Discovery and Recommendation

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An approach for Context-aware Service Discovery and Recommendation

An approach for Context-aware Service Discovery and Recommendation

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  • 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..