This document summarizes a workshop on data integration using ontologies. It discusses how data integration is challenging due to differences in schemas, semantics, measurements, units and labels across data sources. It proposes that ontologies can help with data integration by providing definitions for schemas and entities referred to in the data. Core challenges discussed include dealing with multiple synonyms for entities and relationships between biological entities that depend on context. The document advocates for shared community ontologies that can be extended and integrated to facilitate flexible and responsive data integration across multiple sources.

1. Industry Programme Workshop: Data Integration
18-19 September 2013
Ontology-based
data integration
Janna Hastings

2. Data integration is hard
Technology
Syntax
Semantics
Content

3. Different data resources, different needs
“why can‟t they all just use the same
- schema
- measurement accuracy
- units
- labels
- content?”

4. Standards are the solution… (?)
Source: http://xkcd.com/927/

5. Ontology-based data integration
Ontologies can help with the semantic and the content
aspects of data integration
• Semantic: definition for schemas
• OWL is a good language for defining schemas
• See RDF and Semantic Web presentations, today
• Content: definition of the entities referred to by data
• Ontologies embedded into a data integration workflow help
facilitate content-aware data integration

6. Core challenge: labelling
Multiple labels can mean the same thing
One label can mean multiple things

7. Semantics-free identifiers, multiple
synonyms
CHEBI:27732
A trimethylxanthine in which the three methyl
groups are located at positions 1, 3, and 7.
guaranine methyltheobromine
1,3,7-trimethylxanthineKoffein
caféine

8. Core challenge: biological knowledge
The answer to the question: “Is
Entity A from Data Source 1
the same thing as
Entity B from Data Source 2?”
often depends who is asking and who is answering!
Left lung vs. lung
Hippocampus vs. brain
Dopamine vs. L-dopamine
In vitro vs. In vivo cells of type X
Gene Y and post-translationally modified form Y‟
Gene Z in mouse, Gene Z in human

9. Hierarchy
left lung
lung
organ
is a
is a
Generalise to the
nearest common ancestor
i.e. if you are integrating data about tissue
samples annotated to „lung‟ in the one
dataset, and „left lung‟ in the other,
The ontology can compute „lung‟ as the
nearest common ancestor
Also for „left lung‟ and „right lung‟

10. Other relationships
Relationships encode biological knowledge
Rules allow to specify which relationships
can be traversed for data integration purposes
e.g. for tissue samples, part_of:
sample_frompart_of => sample_from
A sample from a part of the brain (e.g. the
hippocampus) is a sample from the brain
(Quite aside from the „is a‟ hierarchy!)
brain
hippocampus
part of

11. Core challenge: flexibility
… (>150 members)
Fixed-depth hierarchies
force some classes to be
too big, with the lowest level
collapsing biolgoical hierarchy
and others too small
… (<1 member)

12. Ontologies in content integration
A
B
A&B
1. Schema
mappings
A
B
2. Ontology-
provided
synonyms
A
B
3. Hierarchy
and relationship
rules for integration
OWL language and tools: web-embedded
(but whole-ontology rule reasoning may be slow)

13. Is ontology integration
just another type of data integration?
Which ontology(-ies) to use?
How to use them together?
How to plug the gaps?
Why should I (as a user) have to
do this integration over and over

14. Desiderata for ontologies for data
integration
• Ontologies should be neutral and shared community-
wide
• Users should be able to directly and rapidly extend the
ontology where there are gaps (responsiveness)
• The ontology should use semantics-free identifiers and at
the same time energetically annotate synonyms
• When necessary, ontologies should take care of
ontology integration to provide the community with a
one-stop service and appropriate cross-references
• The ontologies should be used
in data annotation
See http://www.obofoundry.org/

15. Questions?