1. Tackling the Curse of
Prepayment
Collaborative Knowledge
Formalization Beyond Lightweight
Valentin Zacharias & Simone Braun

2. The Curse Of Prepayment
The Curse Of Prepayment
Semantic
Semantic
Technologies promise
great functionality,
great functionality
once a great amount
of knowledge is
formalized

3. FolksOntology
lk l
IkeWiki

4. Simple Collaborative
Incremental Partial
Immediate

5. Making Every Penny Count –
Immediately
• Immediate benefit for formalizing
even small parts, becomes
ll t b
justification for next part
– Tables, Hierarchical Organisation,
Advanced Search
• D
Datastore never fully formalized
f ll f li d
• Formalizing everything impossible

6. Text &
& Informat
tion Retrieval
Tags &
& Keyword
ds
Semi S
Structured Dat (e.g
g. Wiki)
Taxonomies
Thesa
auri + Skos
s
Web 2.0 Ontology Engineering
Structu
ured Data (e.g. Free
ebase)
Rule La
anguages
OWL D
DL
The Problem of the Brick Wall
The Problem of the Brick Wall
FOL
Ontologies

7. What Weight Problem?
What Weight Problem?
• Usability/Debuggability
• Robustness
• Performace
• Language Expressivity
Language Expressivity
vs. Performance – and
is this even the right
is this even the right
question?
• Mixed/ semi formality
Mixed/ semi‐
Partly based on: Krötzsch, Schaffert, Vrandecic: Reasoning in Semantic Wikis

8. Curse Of Prepayment Again
Curse Of Prepayment ‐ Again
• What is the goal, what is the ‘immediate
g p
benefit of formalizing even small parts’?
– Better retrieval? (e.g. clicking on a tag also returns
things annotated with subtags)
things annotated with subtags)
– Better Navigation? (e.g. when I browse through
my bookmarks)
my bookmarks)
– Organization and Maintainability? (e.g. by defining
data only once and reusing it in tables)
d t l d i it i t bl )

10. ‘Collaborative Incremental
Augmentation of Text Retrieval’

11. ‘Collaborative Incremental
Augmentation of Text Retrieval’

12. ‘Collaborative Incremental
Augmentation of Text Retrieval’

13. ‘Collaborative Incremental
Augmentation of Text Retrieval’

14. ‘Collaborative Incremental
Augmentation of Text Retrieval’

15. Collaborative, Incremental Augmentation of Text Retrieval =
–S i E i f‘
Step wise Extension of ‘normal’ text retrieval in the direction of
l’ i l i h di i f
Question Answering
– Understanding the NL Query < > System query mapping also as
Understanding the NL‐Query <‐> System query mapping also as
collaboratively matured artifacts.
Potential Advantages
• Reasonable E pectations
Expectations
• Incremental and Partial
• Immediate
• Accepted Interface
p
Please see the paper for a bit more technical detail
on how to realize this

16. • Collaboration: System functionality is
created during use by the users (not before
by programmers)
by programmers)
• Incremental: Functionality to answer
q
questions is added step by step; back up
p y p; p
information retrieval engine avoids
disappointment
• Existing Queries: Not ‘speak to the
Existing Queries: Not speak to the
machine’, rather detection of queries done
anyway

17. • ‘Web2.0 Ontology gy
Engineering’ tries to tackle
the Curse Of Prepayment
with a kind of KF where
ith ki d f KF h
every bit of formalization
counts
• Supporting more
heavyweight formalisms
y g
entails many challenges, but
most important is the
finding of new answers to
fi di f t
the Curse of Prepayment
• ‘Collaborative Incremental
Collaborative Incremental
Augmentation of Text Thanks for your attention
Retrieval’ could be one such
answer. Valentin Zacharias
V l ti Z h i
Simone Braun