Linked Science as a producer and consumer of big data in the Earth Sciences

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1 Linked Science as a producer and consumer of big data in the Earth Sciences Line C. Pouchard,* Robert B. Cook,* Jim Green,* Natasha Noy,** Giri Palanisamy* Oak Ridge National Laboratory* Stanford Center for Biomedical Informatics Research ** Presented to the Ontology Summit 2012 March 8, 2012

2 What is Linked Science? Scientific collaborations are growing and becoming more interdisciplinary, Linked Science is: A practice that accounts for objects of study from an end-to-end perspective exhibits key trends in the role of data means of communication between stakeholders are key characterized by experimental, theoretical and simulation methods systematic emphasis on validated datasets, value-added data products, traceable information and integrated processes First Linked Science Workshop, International Semantic Web Conference, October 2011 Second Linked Science Workshop, ISWC, October 2012, Boston 2

3 Linked Science in the Earth Sciences Coupled Climate-Land Surface Model FLUXNET Forest Biomass Forest Biomass Coupled Climate-Land Surface- Hydrology Model Discovery and access from heterogeneous sources Simulations, models, experiments, remote sensing, GIS, molecular and omics databases, publications Metadata and semantics integration Workflows, scenario development, data and process re-use, provenance Engaging communities of scientists, educators, librarians, developers, volunteers Relies upon cyber-infrastructure promoting open source Complex systems of systems, networks of projects, repositories, archives, publishers IPCC Report 3

4 Near-term future nodes Existing nodes Metadata Extraction 4 Mercury: federated metadata engine for earth sciences data NBII BDP LTER EML USA-NPN BDP ORNL DAAC FGDC OBFS IAI LP DAAC EML FGDC DIF Internal Metadata Index LBA I-LTER TERN SAEON FGDC Stored at Mercury Server

5 Coupling the Mercury federated metadata engine and BioPortal Query Controller Transaction Display Ontology Index Uses BioPortal Rest s for programmatic access Returns ontology concepts, super- and subclasses Provides additional keywords Provides context Uses these for new searches 5

6 BioPortal provides access to ontologies Domain ontologies are used within the engineered system to improve facetted search results 6

7 Ontology-based search results Concepts acquire context: biomass as Material or biomass as Energy Additional search terms Super-classes may have different properties 7

8 Metrics assessing the impact of using ontologies in search y = frequency x = count of index terms Matching the top 100 Mercury parameters to ontology terms Frequency count: 79% of the Top 100 keywords have at least one match in the chosen ontologies N = 99, 2 values missing (plant, leaf) water : 38 air, carbon = 28 8

9 Lessons Learned User-friendly display Current display may be confusing. What are the options? send the user to a new page implement a new display dynamically driven by ontology relationships Ontology content SWEET provides a good basis, but needs to be further specified for the needs of this Data Center Many ontologies provide only few relationships Implementation Adding ontology entities to a keyword index helps with recall but cannot substitute for semantic annotations of the metadata documents 9

10 Thank you ORNL DAAC and Mercury ORNL DAAC ontology service ORNL DAAC instance of BioPortal Stanford Center for Biomedical Informatics Research BioPortal Stanford Center for Biomedical Informatics Research Protégé ontology editor The submitted manuscript has been authored by the US Department of Energy, Office of Science of the Oak Ridge National Laboratory, managed for the U. S. DOE by UT-Battelle, LLC, under contract No. DE-AC05-00OR

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