OBOE: An ontology for describing & synthesizing ecological data

1. OBOE: An ontology for describing & synthesizing ecological data Knowledge Representation Working Group

2. Ecological research • Research in ecology increasing relies on the synthesis of data (physical, chemical and biological) • Problem: data are heterogeneous; details not recorded • Metadata standards important first step, but don’t capture all necessary aspects of data content • Solution: map data and metadata to a formal model that captures their meaning (or semantics)

3. Motivation Formal information is needed to: • Comprehensively discover data • Search for and access relevant data • Rapidly interpret, summarize and view data • Automatically integrate data • Automatically determine if data are compatible • Calculate appropriate conversions to merge data

4. Definitions • Observation: • An assertion of the existence of an entity, by an observer (human or non-human), typically resulting in one or more measurements of characteristics of that entity. • Observations can provide context for other observations. • Entities can be biotic (e.g., animals) and abiotic (e.g., water) • Observational data: • Any recorded measurements resulting from observations

5. Observation ? A assertion that an entity exists

6. Entity All things concrete and conceptual

7. Entity An extension point for domain-specific terms

8. Measurement Observations can result in measurements of characteristics of the entity

9. Characteristic

10. Measurement The raw data Measurement assigns a value, via a measurement standard, to the characteristic

11. Measurement standard All the units, scales, indices, classifications, and lists used for ‘measuring’ a characteristic

12. Example Measuring the height (characteristic) in meters (standard) of an tree (entity)

13. Context Observations can provide context for other observations

14. Context Context is transitive; measurements can be made at each level of observation

15. Model extensibility • OBOE provides a core framework for organizing domain concepts • Entities, Characteristics and Measurement Standards • Developing extensions • Units • Top-level ecological concepts (textbook parsing) • Structured controlled vocabularies (LTER)

16. Semantic annotation

17. Semantic annotation Example data set: the abundance of Trapeziid crabs in coral colonies (Stewart et al. 2006)

18. Semantic annotation

19. Semantic annotation

20. Semantic annotation

21. Semantic annotation

22. Applications overview • Core OBOE ontology definitions (complete) • Semantic annotation mechanism (prototype) • Visualization of observational structures (prototype) • Semantic search and ranking (prototype) • Automated data summarization (development) • Data integration (active research & development)

23. Semantic annotation

24. Simple Keyword Search

25. Broader architecture EML Semantic Annotation Scientists & other end users Metadata Editing Data Discovery Data Browsing Creation & Managementof Standard Ontologies Community-Driven (Collaboration Among Scientists & InformaticsSpecialists) Federated Metadata & Data Management Back-End System Scientists & other end users Data Discovery Data Browsing

26. Summary • OBOE is an ontology framework for describing observations of entities, their measurements, and context • OBOE provides a structured approach for incorporating domain ontologies • OBOE is used to semantically annotate observational data • OBOE provides necessary constructs for discovering and integrating the diverse range of data

27. Acknowledgements • Knowledge Representation Working Group • Mark Schildhauer, Matt Jones (NCEAS) • Shawn Bowers, Bertram Ludaescher, Dave Thau (UCD) • Deana Pennington (UNM) • Serguei Krivov, Ferdinando Villa (UVM) • Rich Williams (Microsoft)