1. University of Maryland, Baltimore County
DReggie:
Semantic Service Discovery for
M-Commerce Applications
Dipanjan Chakraborty
Filip Perich
Sasikanth Avancha
Anupam Joshi

2. Today: Life is easy

3. Tomorrow: We Got Problems

4. Service Discovery Techniques
Unique ID based
Attribute-based
Interface-based
Some existing discovery architectures
Jini
Salutation
SLP
UPnP
UDDI
Bluetooth SDP

5. NO Is this enough? Lack of rich representation
Lack of constraint specification and inexact matching
Lack of ontology support
Resource considerations for mobile devices

6. DReggie: A semantic Service Discovery System
Built on Jini Technology
Uses DARPA Agent Markup Language (DAML) to describe services
Services matched using attribute, interface and their semantic description
Simple Java-based light-weight reasoner and a complex Prolog-based reasoner
Inexact matching, resource-based matching

7. DReggie Architecture Mobile Device DAML Register Service DAML
Lookup Server
Service Description
Matching
Module
DAML
Service
Register
DAML
Request
Mobile
Device
Reply
Reply
Reply
Register
DAML
Service

8. Ontology Details
Use resource description capabilities of DAML+OIL to represent services and request
An Ontology to represent services in terms of
Capabilities
Input/Outputs
Platform dependencies
Current resources
Mobility

9. Java-based simple reasoning module for lightweight devices
Lightweight Matching
Java-based simple reasoning module for lightweight devices
Module parses the DAML request
Tries to match the ‘nearest’ matching service
Takes into consideration resource limitations and dependencies of services

10. Heavyweight Matching
Heavyweight Prolog-based reasoning module for resource-rich devices
Parses the DAML ontology, service profile and loads the facts into its knowledge base
Parses the request and uses inference techniques to match for services
Capability to match services similar to the one requested

11. DAML+OIL (DARPA Agent Markup Language +Ontology Inference Layer)
Why DAML+OIL?
DAML+OIL (DARPA Agent Markup Language +Ontology Inference Layer)
Building tool for the semantic web
Rich description capability to describe resources/services
Incorporation of simple rules
Built on top of RDF/XML

12. Limitations/Future Directions
Directory-based service discovery
Peer-to-peer discovery?
Ad-hoc environment
Network/language dependence
Future Directions
Richer matching capabilities to the Prolog reasoner
Peer-to-peer discovery in ad-hoc environments

13. Ebiquity Research Group at UMBC
Resources
Service Ontology
Ebiquity Research Group at UMBC

14. Ebiquity Research Group