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WP2: ONTOLOGY ENRICHMENT METHODOLOGIES

Current bio-ontologies lack expressivity, impeding their utility for biologists. This work introduces ontology enrichment methodologies focused on enhancing semantics through tools such as Ontology Design Patterns (ODPs), normalization techniques, and text mining. By migrating from non-expressive to expressive ontologies, we facilitate reasoning and querying. The implementation of these methodologies includes Biological Ontology Next Generation (BONG) and Ontology Processing Language (OPL). This research aims to provide a structured approach to bio-ontology enhancement, yielding significant benefits for knowledge management and publication.

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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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