1 / 11

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.

bree
Download Presentation

WP2: ONTOLOGY ENRICHMENT METHODOLOGIES

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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.

More Related