1. An Ontology Creation Methodology: A Phased Approach
Jon Atle Gulla
Norwegian University of Science and Technology; Norway
Vijay Sugumaran
Oakland University, USA

2. Agenda Ontology development Traditional ontology learning
Limitations of ontology learning
A phased approach to ontology learning

3. The Challenge How to develop large complex ontologies?
How to keep ontologies updated in dynamic domains?

4. Ontology Modeling vs. Learning
Traditional ontology engineering approach
Project: Form team of ontology and domain experts
Ontology & domain experts: Collaborative manual modeling process
Domain experts: Verify ontology against domain knowledge
Ontology experts: Verify ontology against syntactic and semantic quality measures
Expensive and time-consuming approach
Stable domains assumed
Ontology learning approach:
Domain experts: Find representative domain text
Tool: Extract candidate classes, individuals and properties automatically from domain texts
Ontology & domain experts: Verify candidate structures and complete ontology
Can also be used to verify domain quality of existing ontology
Cost-effective approach
Not unproblematic in dynamic domains

5. Agenda Ontology development Traditional ontology learning
Limitations of ontology learning
A phased approach to ontology learning

6. Ontology Learning Basis
People communicate using domain-specific concepts
People document using domain-specific concepts
Ontology learning: Extract ontology structures from written documentation
Requirements:
Documents representative for domain terminology
Documents cover all the terminology
Well-defined and consistent use of terminology in domain
Ontology discussions
Realm of
ontology engineering
Ontology in use
Realm of
ontology learning

7. Levels of Ontology Learning
Degree of
difficulty
 x,y(manager(x,y) → report(y,x))
Rules
Relations
FINANCE(ag:SPONSOR, go: PROJECT)
Concept hierarchies
is_a(MANAGER, EMPLOYEE)
Concepts
PROJECT
Synonyms
(leader, manager, lead)
Terms
sponsors, costs, charter

8. Ontology Learning Strategies
Term extraction
Linguistic analysis
Statistical analysis
Synonyms
Classification-based techniques
Distribution-based techniques
Concept formation
Structure recognition
Keyphrase generation
Instance learning
Concept hierarchy
Clustering
Lexico-syntactic patterns
Head-modifier approaches
Subsumption approaches
Relations
Association rules
Concept vectors
Rules
Structure recognition for meta-property recognition
Dependency trees and path similarities

9. Ontology Learning Process
Scope management
WBS
Business need
Constituent components
Product description
...
PMBOK
Abstract elements
Constraints
Properties
Rules
Domain text
Concept candidates
Search ontology
Reference set
Automatic extraction of concept and relationship candidates
Manual selection of candidates and completion of model

10. Ex 1. Learning Concept/Individual Candidates
Scope planning is the process of progressively elaborating and documenting
the project work (project scope) that produces the product of the project.
Scope/NNP planning/NN is/VBZ the/DT process/NN of/IN progressively/RB elaborating/VBG
and/CC documenting/VBG the/DT project/NN work/NN (/( project/NN scope/NN )/) that/WDT
produces/VBZ the/DT product/NN of/IN the/DT project/NN ./.
POS tagging
Scope planning is the process of progressively elaborating and documenting
the project work (project scope) that produces the product of the project.
Stopword removal
(571 words)
Scope plan process progress elaborate document project work project scope
produce product project
Lemmatization/stemming
(POS tags not shown)
{scope planning, process, project work, project scope, product, project}
Select consecutive nouns
as candidate phrases
{(scope planning, ), (project scope, ), (product, ),
(project work, ), (project, ), (process, )}
Calculate tf.idf score for phrases

11. Classes Relevant to the Drama Genre
Data sources: IMDB, Wikipedia, Videoload
Keyphrase extraction technique
Noun phrases ranked according to various statistical measures

12. Ex 2. Learning Relationship Candidates
Tokenizer
GATE
Sentence
splitter
Tagger
Lemmatizer
Noun phrase
extractor
Noun
phrase
indexer
Association
rules
miner
Association rules
Concept profiles
Concept
similarity
calculation
profile
builder
Lucene
Document
Paragraph
Light
stemmer
Relationship
merger

13. Relationships Relevant to Drama Genre
Association rules on extracted concepts

14. Automatic OWL Generation

15. Agenda Ontology development Traditional ontology learning
Limitations of ontology learning
A phased approach to ontology learning

16. Limitations of Ontology Learning
Different techniques produce different results
Different data sources produce different results
Lost control over process
Extensive verification of final ontology needed
New data hard to combine with old data

17. Agenda Ontology development Traditional ontology learning
Limitations of ontology learning
A phased approach to ontology learning

18. Ontology Learning for Entertainment Domain
Ontology evolution for Deutsche Telecom’s Videoload download service
What does Brangelina mean?
Should Pitt be Brad Pitt or Michael Pitt?
Actor vs. Schauspieler?
All movies of Brad Pitt?
Last movie of Pitt?

19. Ontology Learning Project
Duration: Nov 2007 – Nov 2009
Domain: movie download service
Ontology analysis and creation based on indexed noun phrases from movie documents
Ontology used for search and navigation on top of FAST search platform
Ontology learning challenges:
Domain changes from one day to another
No consistent domain terminology
No professional domain terminology
Multiple languages
Movies about anything... unlimited domain
Ontology needs to be up to date to support search

20. Ontology Workbench 3 phases that are carried out independently
Crawling into Lucene indices
Supervised extraction of candidates
Combining candidates into ontology structures

21. Interactive Ontology Development
Expandable indices
Subset of data source
Focus of analysis
List of techniques
Partial results
Stored results
Set operations for combining results

22. Thank you