AN APPROACH FOR CONTEXT-AWARE SERVICE DISCOVERY AND RECOMMENDATION

service recommendation

service discovery

Outline

Introduction Our Approach Experiment Conclusion

Outline

Introduction Our Approach Experiment Conclusion

Introduction

Context type location, time

Context Context value New York

Context-aware system: react to a user’s context without their intervention

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

Outline

Introduction Our Approach Experiment Conclusion

Overview of our approach

Overview of our approach

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

Steps of find relevant ontologies

Search with the context

value

Remove the first adj/adv, then search

Annotated the ontology to the context, convert

the remove adj/adv to

constraints

Annotated the ontology

to the context

YES

String is empty

YESNO

Use synonyms of the context

value

NO

Overview of our approach

Identifing context relations

Relations between two Context Values Intersection Complement Equivalence Independence

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

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

Steps of building integrated E-R model

TravelLos

AngelesInterse

ct

TouristAttractions

Integrated E-R model

Steps of building integrated E-R model

Travel LosAngeles

Intersect

TouristAttractions

Integrated E-R model

NBA

Intersect

Los Angele

s Lakers

Steps of building integrated E-R model

Overview of our approach

Generating searching criteria Suppose are entities in the

integrated E-R model. SharedElementsSet represents the set of a user’s needs.

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

Outline

Introduction Our Approach Experiment Conclusion

Experiment

Objective Evaluation of the detected context relations Evaluation of Service Recommendation

Precision， Recall

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

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

Outline

Introduction Our Approach Experiment Conclusion

Conclusion

Use ontologies to enhance the meaning of a user’s context values

The SharedElementSet reflects user’s needs

Experiment is not clear..