Industry Programme Workshop: Data Integration18-19 September 2013

Ontology-based data integration

Janna Hastings

Data integration is hard

Technology

Syntax

Semantics

Content

Different data resources, different needs

“why can’t they all just use the same- schema- measurement accuracy- units- labels- content?”

Standards are the solution… (?)

Source: http://xkcd.com/927/

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

Core challenge: labelling

Multiple labels can mean the same thing

One label can mean multiple things

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-trimethylxanthine Koffeincaféine

Core challenge: biological knowledgeThe 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. lungHippocampus vs. brainDopamine vs. L-dopamineIn vitro vs. In vivo cells of type XGene Y and post-translationally modified form Y’Gene Z in mouse, Gene Z in human

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’

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_frompart_of => sample_from

A sample from a part of the brain (e.g. thehippocampus) is a sample from the brain

(Quite aside from the ‘is a’ hierarchy!)

brain

hippocampus

part of

Core challenge: flexibility

… (>150 members)

Fixed-depth hierarchiesforce some classes to be too big, with the lowest levelcollapsing biolgoical hierarchy

and others too small

… (<1 member)

Ontologies in content integration

A

B

A&B

1. Schema mappings

A

B

2. Ontology-provided synonyms

A

B

3. Hierarchyand relationshiprules for integration

OWL language and tools: web-embedded(but whole-ontology rule reasoning may be slow)

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

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 usedin data annotation

See http://www.obofoundry.org/

Questions?