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.

INTRODUCTION

Current bio-ontologies not very expressive.

Ontology enrichment (migration): add richer semantics.

ODPs, Normalisation, ULO, ...

Text mining.

Ontology enrichment in CCO.

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.

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.

ENRICHMENT

Normalisation.

Ontology Design Patterns.

Upper Level Ontology.

Text mining/learning.

Combination of different ontologies.

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”, ...

ONTOLOGY DESIGN PATTERNS

Simple Example: Value Partition.

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.

NORMALISATION

Let the reasoner do the job:

SUMMARY - BENNEFITS

Tooling.

More expressive CCO: Reasoning.

Querying.

Maintenance.

Area not explored in Knowledge Management: publications.