1. Intelligence Ontology: A Strategy for the Future
Barry Smith
University at Buffalo

2. Semantic Web, wikis, statistical textmining, etc.
let a million flowers bloom
how create broad-coverage semantic annotation systems which will enable sharing of gigantic bodies of heterogeneous data?

3. let a million flowers (weeds) bloom
how create broad-coverage semantic annotation systems which will enable sharing of gigantic bodies of heterogeneous data?

4. let a million microtheories bloom
what about Cyc?

5. why does Cyc not do the job?
#$Configuration   A specialization of both #$StaticSituation and #$SpatialThing-Localized. Each instance of #$Configuration is a static situation consisting of two or more #$PartiallyTangible things of certain types standing in a certain type of spatial relationship (or set of relationships). This (set of) spatial relationship(s) characterizes the #$Configuration's _type_ in the sense that any group of objects of the appropriate types standing in that relationship (or those relationships) correspond to a #$Configuration of that type; and each of these objects, in turn, is said to be configured (see #$objectConfigured) in the (individual) #$Configuration.
why does Cyc not do the job?

6. why does Cyc not do the job?
(speculations)
Cyc doesn’t care about consistency between microtheories
so no progressive cumulation from an established core
too little concern for consistency with basic science
(common sense should not wear the trousers)
no perspicuous policies for updating
built by outsiders
why does Cyc not do the job?

7. Unified Medical Language System (National Library of Medicine)
built by trained experts
massively useful for information retrieval and information integration
good versioning and term-ID policies
creates out of literature a semantically searchable space
Unified Medical Language System (National Library of Medicine)

8. for UMLS local usage respected regimentation frowned upon
mappings between ‘synonyms’ full of noise
is_synonymous_with is not transitive
no cross-framework consistency
no concern to establish consistency with basic science
different grades of formal rigor, different degrees of completeness, different update policies
for UMLS

9. with UMLS-based annotations
we can know what data we have (via term searches)
we can map between data at single granularities (via ‘synonyms’)
how do we combine data across granularities?
how do we resolve logical conflicts ?
how do we know what data we don’t have ?
how do we reason with data ?
with UMLS-based annotations

10. no evolutionary path towards improvement
with UMLS, Cyc, Web 2.0, ...

11. We will be able to use ontologies to help us share data
only if the ontologies represent the world correctly
are humanly intelligible
and computationally tractable

12. a new approach
prospective standardization based on objective measures of what works
bring together selected influential groups to agree on good terminology / annotation habits preemptively

13. for science requirements ensure legacy annotation efforts not wasted
create an evolutionary path towards improvement, of the sort we find in science
must be a collaborative, community effort to ensure buy-in
ensure future-proofing
requirements

14. create a consensus core of interoperable domain ontologies
for science
create a consensus core of interoperable domain ontologies
starting with low hanging fruit and working outwards from there
built and validated by trained experts
backed by persons of influence in different communities

15. for science geospatial transport religion weather bacteria chemicals
politics
law
use common rules drawing on best practices for creating ontologies
... and for linking ontologies

16. for science geospatial transport religion weather bacteria chemicals
politics
law
... exploiting the division of labor
... relying on champions in dispersed communities to spread the words

17. ontology of documents
ontology of provenance
ontology of names
ontology of numbers (IDs)
ontology of signatures
ontology of identity
...

18. that people should use the core to annotate their data

19. and set up feedback mechanisms
annotators discover they need
more terms
more relations between terms
to correct existing relations
the ontology gets better as it is used

20. This process leads to improvements and extensions of the ontology
which in turn leads to better annotations
 a virtuous cycle of improvement in the quality and reach of both future annotations and the ontology itself
This process

21. a growing computer-interpretable map of reality within which major databases are automatically integrated in semantically searchable form
The result

22. This solution is already being implemented in the domain of biomedicine

23. create a shared portal (low regimentation)
First step (2003)
create a shared portal (low regimentation)

25. Second step (2004) reform efforts initiated to link OBO ontologies together and to ensure orthogonality

26. The OBO Foundry http://obofoundry.org/
Third step (2006)
The OBO Foundry
Why suddenly the switch from deep-hell black background to heavenly white ? Is that an intended connotation or just a copy and paste coincidence?

27. some groups commit to working together and to following common rules

28. a family of interoperable gold standard biomedical reference ontologies to serve the annotation of
scientific literature
model organism databases
clinical data
experimental results
The OBO Foundry

29. A subset of OBO ontologies, whose developers have agreed in advance to accept a common set of principles designed to ensure
tight connection to the biomedical basic sciences
compatibility
interoperability
formal robustness
support for logic-based reasoning

30. A prospective standard
designed to guarantee interoperability of ontologies from the very start (contrast to: post hoc mapping)
established March 2006
12 initial candidate OBO ontologies – focused primarily on basic science domains
several being constructed ab initio
A prospective standard

31. Ontology Scope URL Custodians Cell Ontology (CL)
cell types from prokaryotes
to mammals
obo.sourceforge.net/cgi-
bin/detail.cgi?cell
Jonathan Bard, Michael
Ashburner, Oliver Hofman
Chemical Entities of Bio-
logical Interest (ChEBI)
molecular entities
ebi.ac.uk/chebi
Paula Dematos,
Rafael Alcantara
Common Anatomy Refer-
ence Ontology (CARO)
anatomical structures in
human and model organisms
(under development)
Melissa Haendel, Terry
Hayamizu, Cornelius Rosse,
David Sutherland,
Foundational Model of Anatomy (FMA)
structure of the human body
fma.biostr.washington.
edu
JLV Mejino Jr.,
Cornelius Rosse
Functional Genomics
Investigation Ontology
(FuGO)
design, protocol, data
instrumentation, and analysis
fugo.sf.net
FuGO Working Group
Gene Ontology
(GO)
cellular components,
molecular functions,
biological processes
Gene Ontology Consortium
Phenotypic Quality
(PaTO)
qualities of anatomical
structures
obo.sourceforge.net/cgi
-bin/ detail.cgi?
attribute_and_value
Michael Ashburner, Suzanna
Lewis, Georgios Gkoutos
Protein Ontology
(PrO)
protein types and
modifications
Protein Ontology Consortium
Relation Ontology (RO)
relations
obo.sf.net/relationship
Barry Smith, Chris Mungall
RNA Ontology
(RnaO)
three-dimensional RNA
RNA Ontology Consortium
Sequence Ontology
(SO)
properties and features of
nucleic sequences
song.sf.net
Karen Eilbeck

32. Foundry communities include
Transcriptomics (MIAME Working Group)
Proteomics (Proteomics Standards Initiative)
Metabolomics (Metabolomics Standards Initiative)
Genomics and Metagenomics (Genomic Standards Consortium)
In Situ Hybridization and Immunohistochemistry (MISFISHIE Working Group)
Phylogenetics (Phylogenetics Community)
RNA Interference (RNAi Community)
Toxicogenomics (Toxicogenomics WG)
Environmental Genomics (Environmental Genomics WG)
Nutrigenomics (Nutrigenomics WG)
Flow Cytometry (Flow Cytometry Community)
Foundry communities include

33. Organism-Level Process
CONTINUANT
OCCURRENT
INDEPENDENT
DEPENDENT
ORGAN AND
ORGANISM
Organism
(NCBI
Taxonomy)
Anatomical Entity
(FMA, CARO)
Organ
Function
(FMP, CPRO)
Phenotypic Quality (PaTO)
Organism-Level Process
(GO)
CELL AND CELLULAR COMPONENT
Cell
(CL)
Cellular Component
(FMA, GO)
Cellular Function
Cellular Process
MOLECULE
Molecule
(ChEBI, SO,
RnaO, PrO)
Molecular Function
Molecular Process
RELATION TO TIME
GRANULARITY
OBO Foundry coverage
(canonical ontologies)

34. CRITERIA
The ontology is in, or can be instantiated in, a common formal language.
The developers of the ontology agree in advance to collaborate with developers of other OBO Foundry ontology where domains overlap.
The ontology should be useful (have a plurality of user communities).
CRITERIA

35. UPDATE: The developers of each ontology commit to its maintenance in light of scientific advance, and to soliciting community feedback for its improvement.
ORTHOGONALITY: They commit to working with other Foundry members to ensure that, for any particular domain, there is community convergence on a single controlled vocabulary.
CRITERIA

36. for science ORTHOGONALITY
when communities work together to ensure consistency  orthogonality  additivity of annotation frameworks
ADDITIVITY: if we annotate a database or body of literature with terms from one high-quality ontology, we should be able to add annotations from a second such ontology without conflicts
ORTHOGONALITY

37. CRITERIA
IDENTIFIERS: The ontology possesses a unique identifier space within OBO.
VERSIONING: The ontology provider has procedures for identifying distinct successive versions.
The ontology includes textual definitions for all terms.
CRITERIA

38. COMMON ARCHITECTURE: The ontology uses relations which are unambiguously defined following the pattern of definitions laid down in the OBO Relation Ontology.*
* Smith et al., Genome Biology 2005, 6:R46
CRITERIA

39. OBO Relation Ontology Foundational Spatial Temporal Participation is_a
part_of
Spatial
located_in
contained_in
adjacent_to
Temporal
transformation_of
derives_from
preceded_by
Participation
has_participant
has_agent

40. Create an OIC portal: a list of those ontologies and related artifacts which already exist
2. Find out which groups of ontology developers are willing to commit to working towards interoperability

41. 3. Work with these groups in open, on-line and face-to-face, discussions, the records of which are made available on the OIC portal
4. Move towards a suite of authoritative ontologies, one for each domain – stable attractors
5. Make funding depend on use of authoritative ontologies – because these have been shown to work

42. what is a question?
a representation of reality with a hole in it

43. They will provide a representation of the context of reality
within which the holes can appear

44. The OIC Foundry ontologies
will be stable, maximally open resources
They will be authoritative in light of NCOR’s evaluation measures
Will not reveal sensitive capabilities and interests.
They will deal with types (of mobile telephone, of spores, of soil ...)
Not with instances

45. (from Jen Williams, OWI)
Some problems
(from Jen Williams, OWI)

46. Do we have an instruction manual ?

47. create a simple top level framework
Ontology: An Introduction

50. Too few knowledgeable folks, and fewer cleared.
Computer scientists are teaching people ontology tools and ...
Mohammed is_a string
Amount of money is_a integer

51. what we need Training events (summer school ...)
to teach people to CREATE ONTOLOGY CONTENT
to teach people to USE ONTOLOGY CONTENT

52. Joining up will diminish your fiefdom
  If you give everyone the keys to you kingdom, how will you justify your budget?
We can do this already, as a start, with open source resources, plus a few brave champions of good annotation habits
Later we humiliate those who do not join in

53. People use different tools
format/language of the ontology is not easy to understand
the OIC Foundry should use ontologies which are maximally format- and language-neutral

54.
we are to enable sharing of gigantic bodies of heterogeneous data we need all the help we can get
our computers need all the help we can get