1. WP2: ONTOLOGY ENRICHMENT METHODOLOGIES 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/: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.

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.