This document introduces ontologies and their use for automatic data processing. It discusses how data is increasingly complex and shared, requiring annotation, integration and interpretation. Ontologies provide a formal representation of concepts and their relationships to support annotation and interpretation. They define classes, properties and individuals to represent generic knowledge in a domain. While building ontologies is challenging, they are important to support reasoning about annotated data and enable reuse of interpretations.

1. Introduction to ontologies
Olivier Dameron
INSERM U 936 – Université Rennes 1 (France)
http://www.u936.univ-rennes1.fr
2010-11-29

2. Disclaimer
This presentation:
Identifies general problems (also relevant to
ecoOnto)
Explains what ontologies are and how they
can contribute to the project

3. Outline
Automatic (intelligent) data processing
motivations
requirements: annotations, integration,
interpretation
Ontologies
Definition
Principles
Difficulties

4. Automatic data processing

5. Data: evolution
● Increasing quantity (not only in bio world)
● Songs
● Pictures
● Personal notes
● Articles, documentation
● Clinical records
● This trend will probably continue...

6. Data: evolution
● Increased complexity (1/2)
● Pictures
● Metadata
Date, time
●
● Aperture, focus,...
● Author, copyright|copyleft,...
● Tags
● Geotags

7. Data: evolution
● Increased complexity (2/2)
● Clinical records are not what they used to be :-)
● From plain text to structured info
● Refer to external sources (ICD,...)
● Multimedia (pacemaker, images, 3D)
● Soon: genetic info, link to ancestors'
EHR...

8. Data: evolution
● Increased sharing/reuse
● Possible now that data are available
electronically
● Cumulative effect (specially in complex
domains such as bio, with lots of inter-
dependencies)
● Sometimes in purposes not originally forseen

9. Data
● Increased quantity
● Increased complexity
● Increased sharing/reuse
Shifting from direct consumption by humans
to consumption by program(s) for other
programs or for humans

10. Data: requirements
● Annotation
● Integration
● Interpretation

11. Requirement1: data annotation
● Proxy so that the whole dataset does not
have to be examined at each query
● Annotations can be difficult or time-consuming to
produce
● Easier or faster or better results when considering
the annotations instead of the data
● Share not only the data, but their
annotations as well!
● Annotations become data of their own (although we
seldom annotate them :-)

12. Requirement1: data annotation
Figuring out the correct and relevant
information is easy for Homo Sapiens...
Ex: how much does “The Semantic Web primer”
costs?

14. Requirement1: data annotation
Figuring out the correct and relevant
information is easy for Homo Sapiens...
... but difficult for Programus Simplex

16. Requirement2 : data integration
Aggregate and compose information
Ex: how old were the Nebula award winners when
they won the prize?
Ex: how many books had they published?
Ex: average age of the canadian Nebula winners

20. Requirement3 : data interpretation
Google query on owl
Retrieve all the pictures of a sailing boat in a
harbor in Brittany
Retrieve all the radiological exams of a
fracture of the leg

22. Requirement3: data interpretation
Google for “owl”
Noise : owl (bird) VS. owl (DL language)
Silence : a page mentioning “Web Ontology
Language” but not “OWL” would be ignored
How about looking for an OWL ontology
about owls (the birds)? :-)
Annotations are great but not enough
The meaning associated to these annotations is
important too

23. Requirement3: data interpretation
Retrieve all the pictures of a sailing boat in a
harbor in Brittany
:-)
:-(
:-)

24. Ontologies

25. Ontologies: what they are
Ontologies: formal representation of a
shared conceptualization
[Gruber]
[Chandrasekaran]
Annotations underlying structure
Oftentimes, everything that is implicit in a factual
document (clinical record, factual report...)

26. Ontologies: what they are not
Ontology (the branch of philosophy)
Controlled vocabulary, terminologies,...
(although both are useful)
Sets of annotated data (genericity is the key)

27. Ontologies: principles
Individuals: things
They are instances of classes
We hardly see them in ontologies (genericity)...
… except when they represent things that are widely
reused (e.g. geographic entities

28. Ontologies: principles
Properties: binary relation btw individuals
Ontologies can specify domain and range
Additional features : transitivity, functionnality,
symmetry, reflexivity,...

29. Ontologies: principles
Classes: sets of things (think genericity)
e.g. Rabbit (as opposed to Bugs Bunny)
Organized hierarchically (taxonomy) from the more
general to the more specific (multiple inherit. ok)
Inheritance of properties
True path rule: if class A annotates some data, then
all the ancestors of A are also valid annotations
(so if you tag a picture as BugsBunny, you do not
need to mention Rabbit, CartoonCharacter,...)
Can represent constraints on the properties of their
instances

30. Data and ontologies: example
rdfs:subClassOf
Sci-Fi CLASSES
Book
Book General knowledge
(RDFS realm)
rdf:type rdf:type
INSTANCE(S)
Dune
Data-specific,
No generalization
(RDF realm)

31. Data and ontologies: example
The semantics of RDFS
allows us to infer that
Dune is an instance of
Book!
rdfs:subClassOf
(so we do not need
Book Sci-Fi to say it explicitly in
Book the RDF file anymore)
rdf:type rdf:type
Dune

32. Data and ontologies: example
Litterat. Sci-Fi Book
Award Award Person
rdfs:subClassOf
rdfs:subClassOf rdfs:subClassOf
Country
Nebula Sci-Fi Author
Award Book rdf:type
rdf:type United
rdf:type rdf:type States
rdf:type
Dune
citizenOf
Nebula
authorOf
Award wonAward Frank
1965 Herbert

33. Synthesis

34. Synthesis
Annotations are important for efficient data
description
Integration (incl. future reuse)
Interpretration
Focus on describing data as precisely as possible
Ontologies are important for interpreting these
description
General knowledge about a domain
Reusable
Support automatic reasoning

35. Synthesis
Building ontologies is difficult
We have a strong experience in building bad
ontologies
… but having a wide adoption is more important
The lesson learned from Gene Ontology