1. Free Open-Source, Open- Platform System for Information Mash-Up and Exploration in Earth Science Tawan Banchuen, Will Smart, Brandon Whitehead, Mark Gahegan, Sina Masoud-Ansari Center for eResearch & School of Environment The University of Auckland

2. Overview 1.Introduction and background to project 2.Application Development – Software system for integrating, browsing and understanding large information bases 3.Demonstration / sample results 4.Conclusion

3. Components of knowledge computing Rich descriptions of resource meaning Recommender systems Finding analogous situations Knowledge evaluation Ontology alignment tools Filters and query tools for locating resources Knowledge visualization tools (e.g. ConceptVista, CMap, ThinkBase) Workflow description Metadata scraping Ontology capture Use-case capture Tag clouds Ontologies, controlled vocabularies, taxonomies Metadata Knowledge bases RDF/OWL/KIF

4. What is an Ontology? 4 An ontology describes what we know or what is true, via a kind of logic An ontology can be as simple as a concept map showing terms used to describe a topic and the relationships between those terms Topic Terms

5. The problem Knowledge leaks from organizations – Some gets forgotten – Some leaves with its container – Some gets buried or lost in the infrastructure We are very poorly equipped to care for knowledge in computational infrastructure – Can we ‘surface’ more of the knowledge implicitly held in unstructured documents? – If so, can we put it to use effectively?

6. Complete conceptual neighborhood of a document ConceptVista, Gahegan et al.

7. Methods Lab Books Preprints Data Video Blogs Podcasts Codes Algorithms Models Presentations Ontologies Intermediate Results Intermediate Results Related Articles Related Articles Comments & Reviews Comments & Reviews Plans Reproducible, transparent science Composite research components Carole Goble, UK eScience

8. Methods Lab Books Preprints Data Video Blogs Podcasts Codes Algorithms Models Presentations Ontologies Intermediate Results Intermediate Results Related Articles Related Articles Comments & Reviews Comments & Reviews Connections run both ways… an open, linked web of science Plans Carole Goble, UK eScience

9. Application Development Software system for integrating, browsing and understanding large information bases

10. Alfred & SemDat Integration Data Sources Geospatial Data - Geoserver & Mapserver Ontological Data - Sesame Documents - webpages, PDFs, reports Visualization Map Concept graph Concept tree Web browser Analysis methods Visual exploration Relevant measurement Spatial and ontological queries

11. Eclipse Neon uDig Display WebpageDocumentNetworkFlow SPARQL query engine Relevance query engine Display queries Style queries The application has the following basic module types:

12. Single Sourcing

13. Eclipse is used as the base – Stable and industry-standard – Enables advanced coordination between our modules and many available third party modules The display modules provide a view on the dataset with rich interactivity – A user can focus on the information they want. The query engine is the smarts – Determines which information is relevant to the current selection – Determines how that information should be displayed

14. SPARQL queries Styling commands Rich markup for each displayed item Style queries mark-up displayed information based on semantics:

15. Standards: – Eclipse – Industry-standard base with standardized plug-in format NeOn – Existing eclipse application providing useful ontological plug- ins uDig – Existing eclipse application providing useful mapping and browser plug-ins Open source Open standard Active communities – OWL/RDF – Industry standard for representing ontologies – SPARQL – Query language – Jython/Python – Advanced styling and rendering of data

16. Geographic Context (Map View) Analysts can gain insights from geographic relationships between cases Distance – possible physical/chemical interactions, team collaboration Clusters – successes and failures Patterns – successes restricted to a particular team Possible explanations/theories

17. Drill Down to Related Document Analysts can drill down to investigate anindividual abstract/article for more details

19. We need far better information filters

20. Demonstration / sample results

21. Conclusions We are drowning in data / information / knowledge, yet are rewarded for producing more, not less zero sum game: if we are writing more, we must be reading less… Describing documents and other digital artifacts according to a variety of different facets holds considerable promise The semantic web is providing many ways to describe data collections We may not be able to capture what things mean directly, but we can provide some useful signifiers (clues) The traces that individuals leave behind can be very useful, both to themselves and to others. And it is comparatively inexpensive to capture and analyse Trust: Researchers need commitments over data custodianship that they can rely on into the long term. Not 4 year funding cycles for nationally significant datasets

22. Questions? Tawan Banchuen, PhD Lecturer at Auckland University t.banchuen@auckland.ac.nz http://eresearch.auckland.ac.nz