Presentation given at the inaugural meeting of the Concept Web Alliance, 8 May 2009

1. Triples & Access
Jan Velterop

2. “ There is something fascinating about science.
One gets such wholesale returns of conjecture out of
such a trifling investment of fact.
”
Mark Twain, Life on the Mississippi

3. O yeah?
We have far too few returns in terms of usable
knowledge out of such overwhelming investment of
fact!
A lot of fact is deeply hidden!

4. Current Knowledge Transfer
A metaphor
(is Greek for ‘truck’ after all)
Needle transport

5. Information overload?
Too much knowledge?
Stop acquiring it?
Just filtering it?
Or organisation underload?
Lack of conceptual structure?
Unprecedented opportunity?

6. Information overload?
Too much knowledge?
Stop acquiring it?
Just filtering it?
Or organisation underload?
Lack of conceptual structure?
Unprecedented opportunity!

7. Another
metaphor:
What is the use
of water?

8. H2O
Drink
(take in)

9. What is the use
of information?

10. Age to Know
Read
(take in)

11. Publish articles

12. Stretching the
water metaphor:
It’s already
raining – we
must build the
ark

13. The ‘animals’ to come on board:

14. Slide by Carl Lagoze (Cornell) – from this presentation:
http://journal.webscience.org/112/3/orechem.pdf

15. Stretching the
metaphor
further:
If you need
water, rain is
free

16. But if you want
quality control
and
convenience:

17. (node 1, unique ID) (node 2, unique ID)
< Source concept > < Relations (edge) > < Target Concept >
class date value owner condi/on DOI.
All Triples Smart Triples
curated
curated
curated Curated
Remove
Co-occ
Observational
Ambiguity
and
Redundancy
Inferred
Knowledge Space

18. (node 1, unique ID) (node 2, unique ID)
< Source concept > < Relations (edge) > < Target Concept >
class date value author condi/on DOI
}
<Type F1> Database facts (multiple attributes)
<Type F2> Community Annotations F+ C+ A+
<Type C1> Co-occurrence sentence (abstracts e.g. PubMed)
<Type C2> Co-occurrence Full Text (publisher e.g. Springer) C+ A+
<Type A1> Concept Profile Match
<Type A3> Co-expression (gene expression Databases) A+
<Type A4> Modelling hypothesis (e.g. Plectix, InWeb)
Multiple Triples
T-Cell Development
Graph Building (e.g. WikiPathways)
Unique to 101668678
Cancer Promoting Genes
Interleukin-7
Unique to Springer
Unique to Plectix

19. Unique to 101668678

20. (node 1, unique ID) (node 2, unique ID)
< Source concept > < Relations (edge) > < Target Concept >
class date value author condi/on DOI
}
<Type F1> Database facts (multiple attributes)
<Type F2> Community Annotations F+ C+ A+
<Type C1> Co-occurrence sentence (abstracts e.g. PubMed)
<Type C2> Co-occurrence Full Text (publisher e.g. Springer) C+ A+
<Type A1> Concept Profile Match
<Type A3> Co-expression (gene expression Databases) A+
<Type A4> Modelling hypothesis (e.g. Plectix, InWeb)
Multiple Triples
T-Cell Development
Graph Building (e.g. WikiPathways)
Unique to 101668678
Cancer Promoting Genes
Interleukin-7
Unique to Springer
Unique to Plectix

21. (node 1, unique ID) (node 2, unique ID)
< Source concept > < Relations (edge) > < Target Concept >
class date value owner condi/on Etc.
Triples Smart Triples
In these areas significant value
Remove is added to the triples
Curated Ambiguity and
Redundancy
Remove
Observational Ambiguity and
Redundancy
Remove
Inferred; Ambiguity and
constructed Redundancy Knowledge Space

22. The ‘trustmark’
CWATM:
Triple ‘model’
Best practice
Interoperability
Et cetera

23. Download Concept Web Alliance cer/fied triples
Includes edges from:
Pubmed (400,000,000 sentences, 5,000,000,000 concept co-occurrences) (from public data)
Protein databases (UniProt, IntAct, PDB, HPRD – 75,000 human curated PPIs) (from public data)
Gene (co-expression databases (GEO, Express… – 25 square genes) (from public data)
STRING edges (200,000 gene-gene edges) (from semi public data)
InWeb edges (240,000 unique edges from 17 species) (from proprietary data)
Reactome edges (240,000 unique edges from 17 species) (from proprietary data)
Chemspider edges (25,000,000 chemicals) (from semi public data)
Wiki edges (WikEdge = WikiPathways, WikiProfessionals, Omegawiki, Wikigene)
Plectix edges (5,000 extra edges (PPI modeling) (from proprietary data)
Private expression data (3000 extra edges, by Merck) (from proprietary data)
Et Cetera