1. What’s Wrong With current Semantic Web Reasoning (and how to fix it)

2. This talk (and this workshop)
Current state of Web Reasoning?
What's wrong with it?
What are we going to do about it?
LarKC: one large new effort to do something about it

3. What’s wrong with current SemWeb reasoning methods?

4. Characteristics of current Semantic Web reasoning
centralised, algorithmic, boolean, deterministic
examples of current attempts at scaleability:
identify subsets of OWL
OWL DL
OWL DLP
OWL Horst
identify alternative semantics for OWL
e.g. LP-style semantics
scaleability by muscle-power

5. Scalability by muscle power
Task/Data
System
Mill.
Stats.
Time
(sec)
Speed
(stat/sec)
Inference
LUBM(500), [15]
Sesame’s Native Store (v. .0alpha3) 70
10 800
6 481
RDFS +/-
LUBM(600)
SwiftOWLIM v0.92b 83
3 941
20 979
OWL-Horst +/-
LUBM(8000)
BigOWLIM
v0.92b
1 060
4 216
OWL-Horst +/-, complete
Subset of UniProt
ORACLE, 10gR2
100
361
RDFS +
FOAF,DC, PRISM
Jena v2.3
200
no info.
Reif. LUBM + Movie & Actor DB
AllegroGraph RDFS++ Reasoner 1.2
1 000
50 580
19 771
RDFS +/-;

6. Moving in the right direction: New BigOWLIM (OntoText, Sirma)
4 switchable inference modes (owl-max,owl-horst-,rdfs-s, optimised rdf-s, none)
custom rules for definable semantics
< 100ms query performance on billlion triples (but 34hrs upload)

7. Why we need “something different”
Gartner (May 2007, G ):
"By 2012, 70% of public Web pages will have some level of semantic markup, 20% will use more extensive Semantic Web-based ontologies”
Semantic Technologies at Web Scale?
20% of 30 billion 1000 triples per page = 6 trillion triples
30 billion and 1000 are underestimates, imagine in 6 years from now…
data-integration and semantic search at web-scale?
Inference will have to become distributed, heuristic, approximate, probabilistic not centralised, algorithmic, boolean, deterministic
add animation to last bullet

8. Why we need “something different”
FDA white paper Innovation or Stagnation (March 2004):
“developers have no choice but to use the tools of the last century to assess this century's candidate solutions.”
“industry scientists often lack cross-cutting information about an
entire product area, or information about techniques that may be used in areas other than theirs”
Problem: pharmaceutical R&D in early clinical development is stagnating
Current NCBI: linking but no inference
“Show me any potential liver toxicity associated with the compound’s drug class, target, structure and disease.”
(Q1Q2Q3)
Show me all liver toxicity associated with the target or the pathway.
Genetics
“Show me all liver toxicity associated with compounds with similar structure”
Chemistry
“Show me all liver toxicity from the public literature and internal reports that are related to the drug class, disease and patient population”
LITERATURE

9. Why we need “something different”
Our cities face many challenges
Urban Computing is the ICT way to address them
Is public transportation where the people are?
Which landmarks attract more people?
Where are people concentrating?
Where is traffic moving?

10. What’s wrong with current Semantic Web Reasoning
Properties of current inference techniques:
Based on logic as guiding paradigm:
Exact
Abrupt
Expensive

11. Current inference is exact
“yes” or “no”
not: “allmost”, “not by a long shot”, “yes, except for a few”, etc (think of subClassof)
This was OK, as long as ontologies were clean:
hand-crafted
well-designed
carefully populated
well maintained
etc

12. Current inference is exact
But current ontologies are sloppy
(and will be increasingly so)
made by non-experts
made by machines:
scraping from
file-hierarchies,
mail-folders
todo-lists & phone-books on PDA’s
machine learing from examples

13. Sloppy ontologies need sloppy inference

14. Sloppy ontologies need sloppy inference
“almost subClassOf”

15. Combined ontologies need sloppy inference
Mapping ontologies is almost always messy post-doc  young-researcher
“almost equal”

16. Properties of current inference techniques
Based on logic as guiding paradigm:
Exact approximate
Abrupt
Expensive

17. Current inference is abrupt
nothing……………….. yes!
we want gradual answers:
anytime computation
agent can decide how good is good enough (human or machine)
deadline computation
pay for quality
load balancing

18. Current inference is expensive
approximate answers are cheap
gradual answers are arbitrarily cheap (WYPIWYG)

19. Properties of current inference techniques
Based on logic as guiding paradigm:
Exact approximate
Abrupt gradual
Expensive cheap

20. What’s wrong with current Semantic Web Reasoning
obsession with worst-case asymptotic complexity

21. Who cares about decidability?
Decidability ≈ completeness guarantee to find an answer, or tell you it doesn’t exist, given enough run-time & memory
Sources of incompleteness:
incompleteness of the input data
insufficient run-time to wait for the answer
Completeness is unachievable in practice anyway, regardless of the completeness of the algorithm

22. Who cares about undecidability?
Undecidability ≠ always guaranteed not to find an answer
Undecidability = not always guaranteed to find an answer
Undecidability may be harmless in many cases; in all cases that matter

23. Who cares about complexity?
worst-case: may be exponentially rare
asymptotic
ignores constants

24. What to do instead? No good framework for “average case” complexity
2nd best: do more experimental performance profiles with realistic data

25. What’s wrong with current Semantic Web Reasoning
obsession with worst-case asymptotic complexity
not even good framework for "average" complexity
obsession with recall & precision
Why we need “something different”

26. Need for approximation
Trade-off recall for precision or vice versa
security: prefer recall
medicin: prefer precision
Trade-off both for speed
Logician’s nightmare:
drop soundness & completeness!

27. A logician’s nightmare
(Dieter Fensel)
Semantic Web
precision (soundness)
recall (completeness)
logic IR

28. What’s wrong with current Semantic Web Reasoning
obsession with worst-case asymptotic complexity
no good framework for "average" complexity
obsession with recall & precision
no good framework for “good enough”
separation of reasoning and search

29. Integrating Search with Reasoning
Axioms:
a hasType b
b subClassOf c
Reasoning
Conclusion

30. Summary of analysis Based on logic, which is strict, abrupt, expensive
Obsession with complexity
Obsession with traditional soundness/completeness & recall/precision
No recognition that different use-cases need different performance trade-offs

31. The Large Knowledge Collider

32.  Goals of LarKC Scaling to infinity Reasoning pipeline
by giving up soundness & completeness
by switching between reasoning and search
Reasoning pipeline
by plugin architecture
Large computing platform
by cluster computing
by wide-area distribution

33. Scaling to infinity Possible approaches
Markov Logic (probability in the logic, judging truth of formula):
adds a learnable weight to each FOL formula, specifying a probability distribution over Herbrand interpretations (possible worlds)
weighted RDF Graphs (probability as a heuristic, judging relevance of formula):
weighted activation spreading (for selection),
followed by classical inference over selected subgraph
model sampling (probability in the logic): sampling space of all truth assignments, driven by probability of model
and others

34.  Goals of LarKC Scaling to infinity Reasoning pipeline
by giving up soundness & completeness
by switching between reasoning and search
Reasoning pipeline
by plugin architecture
Large computing platform
by cluster computing
by wide-area distribution

35. What is the large Knowledge Collider? Plug-in architecture
Retrieve
Relevant Sources
Relevant Content
Relevant Context
Abstract
Extract Information
Calculate Statistics
Transform to Logic
Select
Relevant Problems
Relevant Methods
Relevant Data
Reason
Probabilistic Inference
Classification
Context reasoning
Decide
Enough answers?
Enough certainty?
Enough effort/cost?
Retrieve
Abstract
Select
Reason
Decide

36. What is the Large Knowledge Collider
Integrating Reasoning and Search
dynamic, web-scale, and open-world
in a plugable architecture
Combining consortium competence
IR, Cognition
ML, Ontologies
Statistics, ML, Cognition,DB
Logic,DB, Probabilistic Inference
Economics, Decision Theory

37.  Goals of LarKC Scaling to infinity Reasoning pipeline
by giving up soundness & completeness
by switching between reasoning and search
Reasoning pipeline
by plugin architecture
Large computing platform
by cluster computing
by wide-area distribution 

38. Two parallel implementations
Medium-size tight cluster parallel computing
≈ O(102) nodes
fully available
fully reliable
(almost) no bandwidth restrictions
Large scale wide area distributed computing
≈ O(104) nodes
unpredictable, unreliable, very limited bandwidth

39. How & when will others get access to the results
Public releases of LarKC platform
Public APIs enabling others to develop plug-ins
Create Early Access Group
Encourage uptake through Challenge Tasks
Encourage participation through
World Health Org. use-case is public domain data
Give access to best practice through contributions to W3C SWBPD, SWEO, HCLS
Note that the Challenge Tasks are not listed as deliverables…

40. Who will build this? Organisation Country DERI Innsbruk Austria
AstraZenica
Sweden
CEFRIEL
Italy
Cycorp, Europe
Slovenia
Universität Stuttgart, High Performance Computing
Germany
Max Plank Psychology
Ontotext Lab
Bulgaria
Saltlux
Korea
Siemens
Sheffield
United Kingdom
Vrije Universiteit Amsterdam
Netherlands
Beijing University of Technology
PRC
World Health Organisation: Cancer Research
France

41. Timing Start in April ’08 First prototype after 1 year
Limited open access after 2 years
Open access after 2.5 years
Open API’s, competition
First demonstrators after 2.5 years
Run-time years

42. Most important results?
“An implemented configurable platform for large scale semantic computing,
together with
a library of plug-ins and APIs enabling development by others,
the practicality of which is shown in
three demonstrated deployments in medical research, drug development and urban computing using mobile data
Open to the community
come and play with us
"Implementation of the platform, and proving that it (i) solves real problems in lecommunications and life sciences and (ii) outperforms the best currently existing technology"