1. Ontologies in Bioinformatics
Robert Stevens
Department of Computer Science
University of Manchester

2. Introduction What is knowledge? What is an ontology?
Relationships between the two communities
The last decade of bio-ontologiesontologies
The future

3. What is Knowledge? man academic, senior ancient university, 5 ratedB I O L G Y
man
academic, senior
ancient university, 5 rated
European
important figure in biology
Knowledge – all information and an understanding to carry out tasks and to infer new information
Information -- data equipped with meaning
Data -- un-interpreted signals that reach our senses
Name
Job
Institution
Country C o n f
“Data are the uninterpreted signals that reach our senses every minute in time by the zillions…Information is data equipped with meaning…Knowledge is the whole body of data and information that people bring to bear to practical use in action, in order to carry out tasks and to create new information.” (Schreiber et al. 1998)
Michael Ashburner
Professor
University of Cambridge UK I S M B

4. Things, Symbols & Concepts
Humans require words (or at least symbols) to communicate efficiently. The mapping of words to things is only indirectly possible. We do it by creating symbols that stand for things.
The relation between symbols and things has been described in the form of the meaning triangle:
“Jaguar“
Concept
[Ogden, Richards, 1923]
Concept refers to thing
Symbol stands for Thing
Symbol evokes concept
Symbol: “Jaguar”
Thing: car or beast

5. Representing Knowledge
Language uses symbols and rules (natural language) to communicate knowledge
Need human intelligence to deal with pragmatics
NLP notoriously difficult
Need to capture knowledge in a computationally amenable manner
Ontology: A conceptual model
Ontology plus lexicon is a terminology
Primary aim of creating a shared understanding of a domain and the relationships within that domain
Common symbols for the things within a domain
Capturing domain knowledge with fidelity and precision

6. Sharing info  Sharing meaning
Metadata
Data describing the content and meaning of resources and services.
But everyone must speak the same language…
Terminologies
Shared and common vocabularies
For search engines, agents, curators, authors and users
But everyone must mean the same thing…
Service
provider
The WWW has made data available:
Ready publication
An infrastructure for retrieving and representing documents
An infrastructure for accessing data
Next Step is semantic interoperation:
Understanding what the data means
Linking in insightful ways
Automated support for integration
Sharing data  Sharing meaning
Ontologies are often used as controlled vocabularies
To share and integrate information you must describe it.
Ontology: From knowledge representation and philosophy
A rigorous and explicit conceptualisation of knowledge
Linked to words to render the concepts
Shared controlled vocabulary
Used for:
Complex and expressive conceptual descriptions of data
Subject classifications
Reasoning and inferring new knowledge
Sharing & exchanging knowledge
Much of the biological data is self-described marked up text (pre-dating XML), and hence ontologies for disambiguating database entries and annotation is accepted as standard practice.
Disambiguating content.
Ontologies
Shared and common understanding of a domain
Essential for search, exchange and discovery

7. What is an Ontology?
Concepts: Units of thought: Classes and individuals;
Protein, Gene, DNA, Hexokinase, glycolysis,…
Terms: Labels for concepts “Protein”, “Gene”,…
Relationships: Semantic links between concepts
Is-a-kind, is-a, part-of, name-of,…
Taxonomy backbone of ontology

8. So what Counts as an ontology? [Deborah McGuinness, Stanford]
General
Logical
constraints
Frames
(properties)
Formal
Is-a
Thesauri
Catalog/ ID
Disjointness,
Inverse, partof
Formal
instance
Informal
Is-a
Terms/
glossary
Value
restrictions
Arom
Gene Ontology
TAMBIS
EcoCyc
Mouse Anatomy
PharmGKB

9. The art of ranking things in genera and species is of no small importance and very much assists our judgment as well as our memory. You know how much it matters in botany, not to mention animals and other substances, or again moral and notional entities as some call them. Order largely depends on it, and many good authors write in such a way that their whole account could be divided and subdivided according to a procedure related to genera and species. This helps one not merely to retain things, but also to find them. And those who have laid out all sorts of notions under certain headings or categories have done something very useful.
Gottfried Wilhelm Leibniz, New Essays on Human Understanding

10. The Gene Ontology

11. Bio-Ontologies in the Past Decade
Explicit use of ontologies fairly recent
EcoCyc and RiboWeb using Frame Based Systems to create knowledge bases
An area in which the CS community can test their technology
Large, complex and dynamic
“A knowledge based discipline”
The post-genomic era encourages the need for shared understanding
Cross-genome comparisons need structured, controlled vocabularies
Moved from small nich to a much bigger niche
Biologists are building ontologies

12. Uses of Bio-Ontologies
Controlled vocabularies for annotation
Describing schema dn the content of schema
Domain maps
Query mechanisms
Resolution of semantic heterogeneiety
Text analysis….

13. The Gene Ontology
Tutorial and the first Bio-Ontologies meeting at ISMB 1998 in Montreal
Fly, mouse and yeast get together to develop GO
First release some 3,500 terms covering Molecular Function, biological Process and Cellular Component
Now some 15,000 terms and growing
Gene Ontology Consortium covers some 15 organism databases plus SWISS-PROT and others
Synonyms, abbreviations and associations to gene products: Access to names, genes etc.
A common understanding across a community

14. GO DAG for heparin biosynthesis
GO: : Gene_Ontology (46199)         GO: : biological_process (30188)              GO: : cell growth and/or maintenance (20547)                    GO: : metabolism (14693)                          GO: : carbohydrate metabolism (267)                                   GO: : aminoglycan metabolism (18) GO: :glycosaminoglycan metabolism
GO: : heparin metabolism (3)
                                           GO: : heparin biosynthesis (3)                                                 
Original DAG for heparin biosynthesis as of July 02. The DAG shows heparin biosynthesis has only one parent and there is only one path to the root concept. All relationships are is-a except between Gene ontology and biological process. This is actually a mixed classification organised both in terms of the chemical being metabolism and the nature of the process. So the direct parent glycosaminoglycan metabolism is more abstract both in process and chemical class. However there is no link to the biosynthesis tree within the ontology.

15. Open bio-Ontologies (OBO)
Go, though large, is narrow
Sequence Ontology
Chemical Ontology
Promotes a common ontology format, tools and house-style
Micro-array community a further boost – avoiding mistakes of previous bioinformatics resources
Need ontolgoies for phenotype, tissues, anatomies, etc.

16. Two Communities Computer Scientists Biologists Better Ontologies
Building ontologies KR
Reasoning
Biologists
Ontology content
Domain Knowledge
Better Ontologies

17. What are We Saying? Person Man Woman
is-a
is-a
Man
Woman
Are all instances of Man instances of Person?
Can an instance of Person be both a Man
and an instance of Woman?
Can there be any more kinds of Person?

18. This Year’s Meeting A theme of text analysis and ontology
First time talks have matched theme
Ontologies and indexing
Integrating ontologies into NLP systems
Ontologies in information retrieval
Developing terminologies
GO in NLP
New Ontologies
Semantic Similarity

19. Opportunities Ontologies to help text analysis
Text analysis to help build ontologies
Biology community steadily building a large number of large domain ontologies
CS community can help build computationally amenable ontologies
Vast quantities of domain knowledge in natural language forms in literature and databanks
Opportunities for language and ontology communities