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Ontological realism as a strategy for
integrating ontologies
Ontology Summit February 7, 2013
Barry Smith
http://ontology.buffalo.edu/smith
1
• Scientific data is stored in databases
• There are very few constraints on the
creation of new databases
• Scientific data becomes ever more siloed
• Proposal to counteract this silo-formation:
create a common non-redundant suite of
ontologies covering all scientific domains
to annotate scientific data
• Question: What strategy shall we use to
build this suite of ontologies
2
More precisely:
How shall we build a common ontology =
an ontology that will integrate well with
ontologies built for neighboring domains?
This question arises in science, but it arises
also in other domains, such as commerce,
government and the military
3
The Semantic Web provides only
a small part of the solution
• html demonstrated the power of the Web to
allow sharing of information
• can we use semantic technology to create a
Web 2.0 which would allow algorithmic
reasoning with online information based on
XLM, RDF and above all OWL (Web Ontology
Language)?
• can we use RDF and OWL to break down silos,
and create useful integration of on-line data and
information
4/24
Ontology success stories, and
some reasons for failure
•
A fragment of the “Linked Open
Data” in the biomedical domain
5
Not all of the links here
are what they seem
The more Semantic Technology is
successful, they more it fails to
solve the problem of silos
Indeed it leads to the creation of multiple,
new, semantic silos – because multiple
ontologies are being created in ad hoc ways
6/24
7
8
9
There are many ways to create ontologies
10
How do we build ontologies so that they converge?
(The problem of „knowledge representation‟)
New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
What is going on here ?
ah sh uqwu ja ?
ywye has
uw has!
There are many ways to create ontologies
12
Evidence-based ontology
development
Q: What is to serve as constraint?
A1: Authority (I tell you what to do)
A2: Homesteading (Founder effect)
A3: Best candidate terminology
But what does ‘best’ mean?
A4: Voting ?
But then on what grounds should people vote?
13
A5: Reality, as revealed,
incrementally, by which results of
experimentation become
incorporated into textbook science
14
Three Levels to Keep Straight
• Level 1: the entities in reality, both
instances and universals
• Level 2: cognitive representations of this
reality, e.g. on the part of scientists ...
• Level 3: publicly accessible concretizations
of these cognitive representations in textual
and graphical artifacts
15
New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U
L1
L2
L3
What is going on here ?
ah sh uqwu ja ?
ywye has
uw has!
The realist approach
provides the basis for coordination
(consistency) at the level of specific domains
– reflecting the consistency of textbook
science
But integration requires more than consistency
– it requires also a common realist
methodology for ontology development
Smith and Ceusters, “Ontological Realism as a
Methodology for Coordinated Evolution of Scientific
Ontologies”, Applied Ontology, 5 (2010), 139–188.
17
Benefits of common methodology
No need to reinvent the wheel for each new
domain
Can profit from storehouse of lessons learned
Can more easily reuse what is made by others
Can more easily reuse training
Can more easily inspect and criticize results of
others‟ work
Leads to innovations (e.g. Mireot, Ontofox) in
strategies for combining ontologies
18
Candidate Upper Level Ontologies
– Domain Ontology for Linguistic and Cognitive
Engineering (DOLCE)
– Suggested Upper Merged Ontology (SUMO)
– Upper Cyc Ontology
– Basic Formal Ontology
– all reflections of recognized need for semantic
standardization via upper level ontology
19
Why choose BFO
Very small
Designed to support the consistent representation
of different domains of reality in support of
scientific research
– integration of data via ontologies presupposes
consistent ontologies
Associated with aggressive program of project-
based testing, feedback and training
20
Main reason to use BFO
BFO has the largest body of scientist users
(compare: This telephone network has the
largest number of subscribers)
Snowballing network effects:
data annotated using BFO-conformant
ontologies becomes more valuable
numbers of people with expertise in
building BFO-conformant ontologies
increases
21
Basic Formal Ontology
No fictions
No non-existents
No „possible worlds‟
BFO is designed to support scientific research
• Science is distinct from story-telling
• There is no science of unicorns (though they may
be a branch of psychiatry which deals with
unicorn-obsessions or unicorn-delusions)
22
Basic Formal Ontology
• a true upper level ontology
• no interference with domain ontologies
• no interference with issues of cognition
 a small subset of DOLCE but with a
clearer treatment of instances,
universals, relations and qualities, time
23
RELATION
TO TIME
GRANULARITY
CONTINUANT OCCURRENT
INDEPENDENT DEPENDENT
ORGAN AND
ORGANISM
Organism
(NCBI
Taxonomy)
Anatomical
Entity
(FMA,
CARO)
Organ
Function
(FMP, CPRO) Phenotypic
Quality
(PaTO)
Biological
Process
(GO)
CELL AND
CELLULAR
COMPONENT
Cell
(CL)
Cellular
Component
(FMA, GO)
Cellular
Function
(GO)
MOLECULE
Molecule
(ChEBI, SO,
RnaO, PrO)
Molecular Function
(GO)
Molecular Process
(GO)
The Open Biomedical Ontologies (OBO) Foundry
24
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
(GO)
Cellular Process
(GO)
MOLECULE
Molecule
(ChEBI, SO,
RNAO, PRO)
Molecular Function
(GO)
Molecular
Process
(GO)
rationale of OBO Foundry coverage
(homesteading principle)
GRANULARITY
RELATION TO
TIME
25
Users of BFO (Consortia)
26
NIF Standard Neuroscience
Information Framework
eagle-I Ontologies used by VIVO and
CTSAconnect
IDO Consortium Infectious Disease
Ontology
CROP Common Reference
Ontologies for Plants
– GO Gene Ontology
– CL Cell Ontology
– ChEBI Chemical Ontology
– PRO Protein Ontology
– OBI Ontology for Biomedical Investigations
– OGMS Ontology for General Medical Science
– PATO Phenotype (Quality) Ontology
– IDO Infectious Disease Ontology
– PO Plant Ontology
– SO Sequence Ontology
– FMA Foundational Model of Anatomy
– CARO Common Anatomy Reference Ontology
– EnvO Environment Ontology
– Disease Ontology
The OBO Foundry
27
http://www.ifomis.org/bfo/users
Neurological Disease Ontologies (ND)
Interdisciplinary Prostate Ontology (IPO)
Nanoparticle Ontology (NPO): Ontology for Cancer
Nanotechnology Research
Neural Electromagnetic Ontologies (NEMO)
ChemAxiom – Ontology for Chemistry
Ontology for Risks Against Patient Safety (RAPS/REMINE)
(EU FP7)
Petrochemicla Ontology
US Army (I2WD)
…
28
Example: The Cell Ontology

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Ontological realism as a strategy for integrating ontologies

  • 1. Ontological realism as a strategy for integrating ontologies Ontology Summit February 7, 2013 Barry Smith http://ontology.buffalo.edu/smith 1
  • 2. • Scientific data is stored in databases • There are very few constraints on the creation of new databases • Scientific data becomes ever more siloed • Proposal to counteract this silo-formation: create a common non-redundant suite of ontologies covering all scientific domains to annotate scientific data • Question: What strategy shall we use to build this suite of ontologies 2
  • 3. More precisely: How shall we build a common ontology = an ontology that will integrate well with ontologies built for neighboring domains? This question arises in science, but it arises also in other domains, such as commerce, government and the military 3
  • 4. The Semantic Web provides only a small part of the solution • html demonstrated the power of the Web to allow sharing of information • can we use semantic technology to create a Web 2.0 which would allow algorithmic reasoning with online information based on XLM, RDF and above all OWL (Web Ontology Language)? • can we use RDF and OWL to break down silos, and create useful integration of on-line data and information 4/24
  • 5. Ontology success stories, and some reasons for failure • A fragment of the “Linked Open Data” in the biomedical domain 5 Not all of the links here are what they seem
  • 6. The more Semantic Technology is successful, they more it fails to solve the problem of silos Indeed it leads to the creation of multiple, new, semantic silos – because multiple ontologies are being created in ad hoc ways 6/24
  • 7. 7
  • 8. 8
  • 9. 9
  • 10. There are many ways to create ontologies 10 How do we build ontologies so that they converge? (The problem of „knowledge representation‟)
  • 11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U What is going on here ? ah sh uqwu ja ? ywye has uw has!
  • 12. There are many ways to create ontologies 12
  • 13. Evidence-based ontology development Q: What is to serve as constraint? A1: Authority (I tell you what to do) A2: Homesteading (Founder effect) A3: Best candidate terminology But what does ‘best’ mean? A4: Voting ? But then on what grounds should people vote? 13
  • 14. A5: Reality, as revealed, incrementally, by which results of experimentation become incorporated into textbook science 14
  • 15. Three Levels to Keep Straight • Level 1: the entities in reality, both instances and universals • Level 2: cognitive representations of this reality, e.g. on the part of scientists ... • Level 3: publicly accessible concretizations of these cognitive representations in textual and graphical artifacts 15
  • 16. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U L1 L2 L3 What is going on here ? ah sh uqwu ja ? ywye has uw has!
  • 17. The realist approach provides the basis for coordination (consistency) at the level of specific domains – reflecting the consistency of textbook science But integration requires more than consistency – it requires also a common realist methodology for ontology development Smith and Ceusters, “Ontological Realism as a Methodology for Coordinated Evolution of Scientific Ontologies”, Applied Ontology, 5 (2010), 139–188. 17
  • 18. Benefits of common methodology No need to reinvent the wheel for each new domain Can profit from storehouse of lessons learned Can more easily reuse what is made by others Can more easily reuse training Can more easily inspect and criticize results of others‟ work Leads to innovations (e.g. Mireot, Ontofox) in strategies for combining ontologies 18
  • 19. Candidate Upper Level Ontologies – Domain Ontology for Linguistic and Cognitive Engineering (DOLCE) – Suggested Upper Merged Ontology (SUMO) – Upper Cyc Ontology – Basic Formal Ontology – all reflections of recognized need for semantic standardization via upper level ontology 19
  • 20. Why choose BFO Very small Designed to support the consistent representation of different domains of reality in support of scientific research – integration of data via ontologies presupposes consistent ontologies Associated with aggressive program of project- based testing, feedback and training 20
  • 21. Main reason to use BFO BFO has the largest body of scientist users (compare: This telephone network has the largest number of subscribers) Snowballing network effects: data annotated using BFO-conformant ontologies becomes more valuable numbers of people with expertise in building BFO-conformant ontologies increases 21
  • 22. Basic Formal Ontology No fictions No non-existents No „possible worlds‟ BFO is designed to support scientific research • Science is distinct from story-telling • There is no science of unicorns (though they may be a branch of psychiatry which deals with unicorn-obsessions or unicorn-delusions) 22
  • 23. Basic Formal Ontology • a true upper level ontology • no interference with domain ontologies • no interference with issues of cognition  a small subset of DOLCE but with a clearer treatment of instances, universals, relations and qualities, time 23
  • 24. RELATION TO TIME GRANULARITY CONTINUANT OCCURRENT INDEPENDENT DEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Biological Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Process (GO) The Open Biomedical Ontologies (OBO) Foundry 24
  • 25. 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 (GO) Cellular Process (GO) MOLECULE Molecule (ChEBI, SO, RNAO, PRO) Molecular Function (GO) Molecular Process (GO) rationale of OBO Foundry coverage (homesteading principle) GRANULARITY RELATION TO TIME 25
  • 26. Users of BFO (Consortia) 26 NIF Standard Neuroscience Information Framework eagle-I Ontologies used by VIVO and CTSAconnect IDO Consortium Infectious Disease Ontology CROP Common Reference Ontologies for Plants
  • 27. – GO Gene Ontology – CL Cell Ontology – ChEBI Chemical Ontology – PRO Protein Ontology – OBI Ontology for Biomedical Investigations – OGMS Ontology for General Medical Science – PATO Phenotype (Quality) Ontology – IDO Infectious Disease Ontology – PO Plant Ontology – SO Sequence Ontology – FMA Foundational Model of Anatomy – CARO Common Anatomy Reference Ontology – EnvO Environment Ontology – Disease Ontology The OBO Foundry 27
  • 28. http://www.ifomis.org/bfo/users Neurological Disease Ontologies (ND) Interdisciplinary Prostate Ontology (IPO) Nanoparticle Ontology (NPO): Ontology for Cancer Nanotechnology Research Neural Electromagnetic Ontologies (NEMO) ChemAxiom – Ontology for Chemistry Ontology for Risks Against Patient Safety (RAPS/REMINE) (EU FP7) Petrochemicla Ontology US Army (I2WD) … 28
  • 29. Example: The Cell Ontology

Notes de l'éditeur

  1. http://dbpedia.org/fct/images/lod-datasets_2009-03-27_colored.png