WP2: ONTOLOGY ENRICHMENT METHODOLOGIES

1
WP2 ONTOLOGY ENRICHMENTMETHODOLOGIES

• Carole Goble (IMG)
• Robert Stevens (BHIG)
• Mikel Egaña Aranguren (BHIG)
• Manchester University Computer Science
• IMG Information Management Group.
• BHIG Bio-Health Informatics Group.

2
INTRODUCTION

• Current bio-ontologies not very expressive.
• Ontology enrichment (migration) add richer
  semantics.
• ODPs, Normalisation, ULO, ...
• Text mining.
• Ontology enrichment in CCO.

3
CURRENT BIO-ONTOLOGIES

• Difficult for Biologists to exploit expressivity
  and hence reasoning.
• Label-centered, not model centered
• positive regulation of ubiquitin ligase activity
  during meiotic cell cycle (GO)
• acetylcholine biosynthetic process (GO)
• Ontologies from text mining or database schemas.

4
ENRICHMENT

• From non-expressive to expressive ontologies.
• Progressive.
• Already explored implementations
• Available in http//gong.man.ac.uk/
• Biological Ontology Next Generation (BONG).
• Ontology Processing Language (OPL).
• Based on syntactic/semantic matching.
• Other implementations in the future integration
  of text mining in enrichment.

5
ENRICHMENT

• Normalisation.
• Ontology Design Patterns.
• Upper Level Ontology.
• Text mining/learning.
• Combination of different ontologies.

6
ONTOLOGY DESIGN PATTERNS

• Analogous to OOP design patterns succesfull
  modelling recipes.
• Abstraction of semantics better and easier
  modelling.
• Documented and repeatable modelling.
• CCO new possible ODPs interaction,
  taxonomy, ...

7
ONTOLOGY DESIGN PATTERNS

• Simple Example Value Partition.

8
NORMALISATION

• Hard-coded polyhierarchy
• Difficult to maintain manually add/remove all
  the relationships.
• Not expressive the computer cannot tell why A
  is a subclass of B.

9
(No Transcript)
10
NORMALISATION

• Let the reasoner do the job

11
SUMMARY - BENNEFITS

• Tooling.
• More expressive CCO
• Reasoning.
• Querying.
• Maintenance.
• Area not explored in Knowledge Management
  publications.