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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. WP2: ONTOLOGY ENRICHMENT METHODOLOGIES. INTRODUCTION. Current bio-ontologies not very expressive.

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WP2: ONTOLOGY ENRICHMENT METHODOLOGIES

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  1. Carole Goble (IMG) Robert Stevens (BHIG) Mikel Egaña Aranguren (BHIG) Manchester University Computer Science: IMG: Information Management Group. BHIG: Bio-Health Informatics Group. WP2: ONTOLOGY ENRICHMENTMETHODOLOGIES

  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. NORMALISATION • Let the reasoner do the job:

  10. SUMMARY - BENNEFITS • Tooling. • More expressive CCO: • Reasoning. • Querying. • Maintenance. • Area not explored in Knowledge Management: publications.

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