1. eScience for Sea Science: A Semantic Scientific Knowledge Infrastructure for Marine Scientists
Dr Kristin Stock
Allworlds Geothinking
Centre for Geospatial Science, University of Nottingham
EDINA, University of Edinburgh

2. Introduction
Scientists traditionally search for resources using keyword searches.
COMPASS (the COastal and Marine Perception Application for Scientific Scholarship) is a knowledge infrastructure and interface that provides enhanced discovery.
Employs semantic information and scientific knowledge.

3. Goals Enhanced discovery. Advanced visualisation and inference.
Semantic architecture:
Open standards
Interoperability
Services Oriented Architecture (aka SDI).
Formal ontologies
Informal user tagging.
Different types of resources:
Publications
Data
Web services

4. Outline
Architecture
User Interface
Evaluation

5. The COMPASS Project Funded by UK Joint Information Services Committee
Exploring the use of semantic approaches to discovery and access of resources
Partners:
EDINA
Allworlds Geothinking
University of Muenster
DERI, National University of Ireland, Galway
Geosciences, University of Edinburgh
Finished June 2009

6. Related Work (1)
Keyword search: Google Scholar, Online collaboratories.
Do not allow for search based on scientific methods, theories, etc.
Do not allow for geographic or temporal search.
Do not allow all of these to be combined for discovery and visualisation.

7. Related Work (2)
Foundation work: models of scientific knowledge and discovery (Hars)
Turned into an ontology (SKIo) by Brodaric et al.
Science Commons focuses on security, copyright, licensing.
GEON Geosciences Network – develops a cyberinfrastructure.
People can publish and share resources (data, tools, web services, knowledge).
Supports collaborative research.
Social networking tools (e.g. ResearchGate) includes semantic search.

8. But...
None of these has provided advanced discovery based on science, with ontologies and interoperability.
Hence COMPASS...

9. Software Architecture
Geospatial registry standards: CSW
Registry interface.
No semantic content usually (ebRIM, metadata).
Created an OWL application profile for CSW
Keeps the ontologies in their native form:
Not done like this previously.
Information model = RDF and OWL information models.
Provides standard interface to the ontologies.
Existing reasoning tools can still be used.

11. What does this architecture achieve?
Interoperability through open standards (registry interface)
Between different CSW registries
With digital library standards.
Semantic
Stores OWL ontologies.

12. Information Architecture
All content is in ontologies.
Avoids need for two different structures.
Several different ontologies used.

18. Evaluation: User Experience
Qualitative evaluation with 12 users.
Used the interface and then:
completed the MS Desirability Toolkit
semi-structured interview
Discovery by user tagging (the old way) had the most positive response.
Discovery by domain ontology positive (liked graphical display, connections).
Discovery by scientific knowledge ontology not well understood.

19. Evaluation: Data Population
Need for ontologies a shortcoming
Domain ontology creation: a lot of work!
Application ontologies also a lot of work
Perhaps some parts can be automated
But need to understand the science
Best done by the scientist who created the resource.

20. Evaluation: Architecture
Architecture worked well.
Some effort to make ontologies fit registry interface.
OWL-S very cumbersome.

21. Conclusions Showed how scientific knowledge can be used for discovery.
Generally supported but some improvement needed.
More work needed to help with data population and improve user experience.
Future potential:
Visualisation of scientific patterns
Inference to find trends