Building OWL Ontology Driven Applications

Building OWL Ontology Driven Applications

Jay Kola,10/09/2007

OCHWIZ : A prototype medical application

Why use OWL?

• Good expressive power.• Intuitive for domain experts.• W3C recommendation for

knowledge representation.• Built-in logic services that allow

inferences to be made.

Example : Pigmentation

• Pigmentation has cause Arsenic.• Black Pigmentation has cause Coal tar

constituents (Asphalt, Pitch).• Arsenic is exposed by Glass product

manufacturing and Electronic product manufacturing.

• Coal tar constituents are exposed by Construction industry.

User Questions ?

• What are the causes of Black Pigmentation?

• What are the industries associated with Black Pigmentation?

Arsenic Coal tar constituents Pitch Asphalt

Construction Industry

Glass product manufacturing

Electronic product manufacturing

Table Representation

Clinical Finding Cause

Pigmentation Arsenic

Black Pigmentation Coal Tar Constituents

Black Pigmentation Asphalt

Black Pigmentation Pitch

Cause Industry

Arsenic Glass product manufacturing

Arsenic Electronic product manufacturing

Coal Tar Constituents Construction Industry

Asphalt Construction Industry

Pitch Construction Industry

Clinical Finding Cause

Pigmentation Arsenic

Black Pigmentation

Coal Tar Constituents

Black Pigmentation

Asphalt

Black Pigmentation

Pitch

Black Pigmentation

Arsenic

Blue Pigmentation Silver Salts

Blue Pigmentation Arsenic

OWL RepresentationPigmentation - types Coal tar constituents

Blue Pigmentation - definition

Pigmentation - definition

Construction types

Associations of Pigmentation

Associations of Black Pigmentation

Reciprocal Inferences

is_cause_of some Black Pigmentation

has_cause some Coal_tar_constituent

Give me causes of Black pigmentation

Give me diseases caused by Coal tar or Arsenic

Reciprocal Relationships

• Kills the DL reasoner ….

How to implement Reciprocals Inferences ?• Mirror Ontologies

– One ontology has all relationships in one direction only

– Create two such ontologies. Query each separately. Combine results.

• Use OWL Individuals

Other Reasoner Issues

• Use of disjunctions– D has_cause (A1 or A2 or A3…)

• Scaling problems– FaCT++ is really fast.– Classification time depends on

ontology complexity.

Conclusion

• Reasoner issues can be overcome easily.• OWL offers an intuitive way to model

knowledge.• DL Reasoner service can be integrated

into an application easily.• Makes intelligent application development

easy.• A whole lot of OWL ontologies are

available for download on the web…. GET GOING !