The document summarizes ontology research at the University of Manchester. It discusses defining ontologies using OWL, applying reasoning to classify proteins and detect missing knowledge, building ontologies collaboratively, and using ontologies to model biology and medicine. The research uses ontologies for tasks like protein classification, semantic search, and guiding data annotation in domains like genomics and healthcare.

1. Ontology at Manchester
Robert Stevens
BioHealth Informatics Group
School of Computer Science
University of Manchester

2. 2
Ontology Research at Manchester
Language and
Reasoning
Tools
Modelling

3. 3
So what is an ontology?
Catalog/
ID
Thesauri
Terms/
glossary
Informal
Is-a
Formal
Is-a
Formal
instance
Frames
(properties)
General
Logical
constraints
Value
restrictions
Disjointness,
Inverse, partof
Gene Ontology
Mouse Anatomy
EcoCyc
PharmGKB
TAMBIS
Arom
After Chris Welty et al

4. 4
A Definition
o a set of logical axioms designed to account for the
intended meaning of a formal vocabulary used to describe
a certain (conceptualisation of) reality [Guarino 1998]
o “conceptualisation of” inserted by me
o “Logical axioms” means a formal definition of meaning of
terms in a formal language
o Formal language—something a computer an reason with
o Use symbols to make inferences
o Symbols represent things and their relationships
o Making inferences about things computationally amenable

5. 5
OWL
• Ontologies will form the back bone of the
semantic web
• OWL is the latest standard in ontology
languages from the W3C
• Layered on top of RDF and RDF Schema
• Underpinned by Description Logics

6. 6
OWL represents classes
of instances
A
B
C

7. 7
Interpretations
• Individuals are
interpreted as objects
• Classes are interpreted
as sets containing
objects
• Properties are
interpreted as binary
relations on objects

8. 8
Logical Descriptions
• Class: Water
• EquivalentTo: Molecule that
– madeOf 1 OxygenAtom and
– madeOf 2 HydrogenAtom and
– madeOf only (OxygenAtom or HydrogenAtom)
Class: WaterSubClassOf: Molecule that
hasBoilingPoint value 100 and
hasFreezingPoint value 0 and
hasState some Liquid

9. 9
Reasoning
• These OWL descriptions can be submitted to
a DL reasoner
• Translated into DL
• Checked for consistency—is what we’ve said
satisfiable
• Also infers subsumption hierarchy implied by
statements
• Mistakes all too easy without help
• Formality is your friend

10. 10
Language & Reasoning
• Supporting ontology engineering by automated
reasoning
– Classification
– Consistency checking
– Query answering
• Say the things you want to say and still reason
• Explain reasoning results
• Help debugging unexpected results
• Supporting modularity in ontologies
• Segmenting large ontologies into modules

11. 11
Language & Reasoning
• Inspecting ontologies to find missing
knowledge
• Scalability: Larger ontologies; faster
reasoning; more instances; more
expressivity
• Instance Store: Query answering over
vast numbers of instances

12. Old Protégé (matrix wizard)

13. New Protégé (matrix tab)

14. SWOOP (crop circles)

15. 15
ComparaGRID

16. 16
Classsifying Protein Phosphatases
• Annotating a genome’s proteins is a
bottleneck
• Classifying proteins is a first step to
annotation
• Tools for detecting features
• Need human knowledge to determine class
membership
• Can we capture “how to recognise a
phosphatase” in an ontology?

17. 17
Definition of Tyrosine Phosphatase
Class: TyrosinePhosphatase Complete
(Protein and
- (contains atLeast-1
ProteinTyrosinePhosphataseDomain) and
- (contains 1 TransmembraneDomain))

18. 18
Definition for R2A Phosphatase
Class R2A Complete
(Protein and
- (contains 2 ProteinTyrosinePhosphataseDomain) and
- (contains 1 TransmembraneDomain )and
- (contains 4 FibronectinDomains) and
- (contains 1 ImmunoglobulinDomain) and
- (contains 1 MAMDomain) and
- (contains 1 Cadherin-LikeDomain) and
- (contains only (TyrosinePhosphataseDomain or
TransmembraneDomain or FibronectinDomain or
ImnunoglobulinDomain or Clathrin-LikeDomain or
ManDomain)))

19. 19
Building the Ontology
• Classifications already made by biologists – based
on protein functionality;
• Protein domain composition and other details in
the literature;
• Some 50 classes of phosphatase, 30 protein
domains and 39 relationships;
• ”Value partition” of protein domains (covering and
disjoint);
• Defines range of contains property;
• Literature contains knowledge of how to recognise
members of each class of phosphatase.

20. 20
Incremental Addition of Protein
Functional Domains
Phosphatase catalytic
Cadherin-like
Immunoglobulin
MAM domain Cellular retinaldehyde
Adhesion recognition Transmembrane
Fibronectin III Glycosylation

21. 21
Classification of the Classical Tyrosine
Phosphatases

22. 22
What is the Ontology Telling Us?
• Each class of phosphatase defined in terms of
domain composition
• We know the characteristics by which an individual
protein can be recognised to be a member of a
particular class of phosphatase
• We have this knowledge in a computational form
• If we had protein instances described in terms of
the ontology, we could classify those individual
proteins
• A catalogue of phosphatases

23. 23
Classification of Protein Tyrosine
Phosphatases

24. 24
Results
• Human “gold standard”: Same results plus
two more
• Partially annotated A. fumigatis: Better results
and two new putative phosphatases
• Easily generated and compared phosphatase
profiles
• Parasites
• Whole range of unexpected results---back to
bioinformatics sequence analysis

25. 25
myGrid Service Ontology
• myGrid services and workflow toolkit
• Web service discovery and composition
• Semantic content of provenance
repository
• Wide use of service ontology
• Links wit BioMOBY
• Workflows as knowledge management

26. 26
Informal Modelling
• OWL is formal, but ontology has a long
informal stage
• Tool forms of knowledge elicitation
techniques such as card sorting and
laddering
• Experiments with text to ontology tools
• With suitable text can truncate the informal
stage
• Provide useful starting points for later stages

27. 27
Casual Modelling
• OWL can be scary
• Need the equivalent of pseudo-code
• Work on concept maps as an elicitation
tool
• Convertible to OWL
• Converting spreadsheets to OWL
• Converting thesaurae to OWL

28. 28
Community Building of Ontologies
• Collaboration with University of British
Columbia, Vancouver
• No money and no centre: What can you
do?
• Use your community to build, extend,
check facts in your ontology
• Currently running experiments

29. 29
The Sealife Browser
• An EU project to build a Semantic Grid
browser for the life sciences
• Uses ontology as background knowledge
• Dynamically link to terms on a page
• Link to tools, data, documents, etc
• A semantic shopping cart
• Need to use a broad range of ontologies and
many conversions

30. 30
Modelling Biology & Medicine
• Describing biological phenomena
• Reconciling descriptions
• Analysing biological data
• Describing and analysing healthcare
records
• Guiding annotation: Creating and filling
forms
• Describing medical phenomena

31. 31
Outside Relationships
• BioPAX
• FUGO/OBI
• Plant Ontology
• CBIO
• HL7, …

32. 32
Training
• Introductory OWL tutorials: Non-
biological
• Advanced tutorial: Biology orientated
• Hundreds trained in UK and overseas
(mainly life sciences)
• Hands-on training