1. Lecture #11: Ontology Engineering Dr. Bhavani Thuraisingham
Building Trustworthy
Semantic Webs
Lecture #11: Ontology Engineering
Dr. Bhavani Thuraisingham
September 25, 2006

2. Objective of the Unit
This unit will provide an overview of the software engineering aspects of ontologies. The field is called Ontology Engineering

3. Outline of the Unit
Summary of semantic web technologies discussed so far
Applications discussed so far
Ontology Engineering
Directions

4. Semantic web technologies discussed so far
XML, XML Schema
RDF, RDF Schema
Ontology, OWL
Logic, Rules, Inference
Some discussion of security issues for each technology
Policy specification, Securing documents

5. Types of Application
Horizontal Information Products at Elsevier: Integration
Data integration at Audi: Integration
Skill finding at Swiss Life: Search
Think Tank Portal at EnterSearch: Knowledge man agent
E-Learning: Knowledge management
Web Services: Web services (for any of the other applications discussed)
Multimedia Collection at Scotland Yard: Searching
Online Procurement at Daimler Chrysler: E-Business
Device Interoperability at Nokia: Interoperability

6. Revisiting Ontology Common definitions for any entity, person or thing
Several ontologies have been defined and available for use
Defining common ontology for an entity is a challenge
Mappings have to be developed for multiple ontologies
Specific languages have been developed for ontologies
RDF, OWL, DAML+OIL, etc.

7. What is Ontology Engineering?
Tools and Techniques to
Create Ontologies
Specify Ontologies
Maintain Ontologies
Query Ontologies
Evolve Ontologies
Reuse Ontologies
Incorporate features such as security, data quality, integrity

8. Manual Constructiob of Ontologues
Determine Scope
Consider Reuse
Enumerate Terms
Define Taxonomy
Define Properties
Define facets
Define Instances
Check for Anomalies

9. Reuseing Exitsing Ontologies
The goal is not to reinvent the wheel
Several ontologies have been developed for different domains
Codieid Bodies of Expert Knowledge
Integrated Vocabularies
Upper Level Ontologies
Topic Hierarchies
Linguistic Resources
Ontology Libraries

10. Semi/Automatics Methods for Ontology Generation
Much of the research is focusing on developing ontologies using tools from multiple heterogeneous data sources
Essentially extracting concepts and expanding on concepts from the data sources
Uses combination of data integration, metadata extraction, and machine learning techniques
E.g. Clustering of concepts, Classification of concepts etc.
Text Book describes Semantic Web Knowledge Management Architecture

11. What is Knowledge Management?
Knowledge management, or KM, is the process through which organizations generate value from their intellectual property and knowledge-based assets
KM involves the creation, dissemination, and utilization of knowledge
Ontologies are a form of Knowledge?
Reference: management.htm?source=google

12. Directions Need tools for developing semantic web technologies
XML documents, RDF documents, Ontologies, etc.
How to integrate the multiple ontologjes and tools?
Role of Agents – agents are processes that reasons with semantic web technologies
Semantic web services, data mining, knowledge management integrated