Re-learning Ontology Management for the Web

Chris Welty

IBM Research

Web-based Ontologies

• The “symbols” in the ontology are URIs

• The “meaning” of the ontology is distributed around the web

• The entire ontology may not always be accessible

The Web enables…

• Searching for, ranking ontologies– But not based on meaning

Searching, Ranking, & Quality

• Does quality matter?• Good quality ontologies cost more

– Coverage, correctness, richness, commitment [Kashyap, 2003]– Organization, meta-level consistency [Guarino & Welty, 2000]

[Rector, 2002]– Required for some applications

• Improvements in quality can improve performance [Welty, et al, 2004]– 18% f-improvement in search– Cleanup cost ~1mw/3000 classes– BUT … low quality ontology still improved base

• But to rank quality, it needs to be measured…

The Web enables…

• Searching for, ranking ontologies– But not based on meaning or quality

• Selection, Reuse of ontologies– Including misuse

• Partitioning ontologies– Reusing parts of an ontology

… A problem looms

AT&T Definity System (c. 1992)

• 20,000,000 lines of C code• 1,000 programmers• High turnover

– 25% less than 1 year experience– 75% less than 5

• High reliability requirement – “1 min/year”– “Handle Mother’s day call volume”

• 6+ dimensions of versioning– Country, Language, Major Rev, Minor Rev, Patchlevel, Feature

set• Sales force lack knowledge of cost• Huge maintenance problem

Modern SE

• Packaged components available on the web

• The dream of reuse being realized

• For large projects, the nightmare of reuse being realized– n-dimensions of versioning

• Still, largely w/in control– Can choose when to include the latest jar

The Web Ontology Analogy

• Packaged components available on the web

• The dream of reuse being realized

• For large projects, the nightmare of reuse being realized– n-dimensions of versioning

• If using imports– CanNOT choose when to include the latest

owl:Ontology & Namespaces

• No Semantics– In a real sense, not part of the language– Imports, versioning

• What is an ontology?

• Not packaging mechanisms– Yet used that way

Imports for “Layering”

Upper Ontology

OWL-Time

Fluents Ontology

Events Objects

App-specific view

Imports for Language Levels

RDFS- Axioms

OWL-DL Axioms

OWL-Full Axioms

Key Observation

• OWL&RDF are axiom-based languages– not frame-based or object-oriented

• The definition of a class or property is not in one place (despite some tools)

(Class cdo:CarsDomainObject)(Class cdo:Car partial cdo:CarsDomainObject)

(Class rdo:RacingDomainObject(Class cdo:Car partial rdo:CarsDomainObject)

Separating axioms by language(Class RigidClass partial (restriction oc:subClassOf allValuesFrom (complementOf(AntiRigidClass)))(Class NonRigidClass partial)(disjointClasses RigidClass NonRigidClass)

(rdfs:subClassOf RigidClass owl:Class)(rdfs:subClassOf NonRigidClass owl:Class)(sameAs oc:subClassOf rdfs:subClassOf)

(RigidClass cdo:Car)

The Dark Side

(Class oc:RigidClass partial restriction oc:subClassOf allValuesFrom complementOf(oc:AntiRigidClass))(Class oc:NonRigidClass partial)(disjointClasses oc:RigidClass oc:NonRigidClass)

(Class oc:RigidClass partial oc:nonRigidClass)

What does it mean?????

Wherefore Reasoning?

• “Glorified Compiler”• Build a taxonomy [Rector]• …

• The “user community” is still unsure what the purpose of reasoning is

A looming problem

• Prediction– Ontology maintenance will become the

significant problem as ontologies become more mainstream

– Will follow the SE model (80% of cost)

• Observation/Conjecture– High quality ontologies are easier to maintain

Software Maintenance

• Fixing Bugs

• Testing

• Enhancing

Ontology Maintenance

• Fixing Bugs– Inconsistent– Inaccurate– Inefficient

• Testing– Regression tests– Test Suites– Meta tag sets for test

content– Ablation tests

• Enhancing– Tweaking

• Richness• Correctness• Organization• Meta-level consistency• Efficiency

– Extending• Improving coverage• Extending commitment• Integration

– Refactoring

Of Chickens and Eggs

• Many other fields focus on large information artifacts– DB, DL, SE

• Other fields of information processing have hit a “wall”– IR, NLP, semantic integration

• Guess where they’re looking for help?