1. Linking Disparate Datasets of the Earth Sciences with the SemantEco Annotator Session: Managing Ecological Data for Effective Use and Reuse Patrice Seyed 1,2, Katherine Chastain 1, and Deborah McGuinness 1 1 Tetherless World Constellation, Rensselaer Polytechnic Institute, 110 8 th Street, Troy, NY 12180 2 DataONE, University of New Mexico, 1 University Boulevard N.E., Albuquerque, NM 87131

2. Overview Introduction Semantics and Linked Data Use Case: SemantEco SemantEco Annotator –Concept –Getting started –Overview Ontologies Capabilities Integration with Semantic Applications Future Work Quick Look Video Summary 1

3. Introduction How can we take datasets from different sources and make them –Easy to search and to discover? –Easy to use and to re-use? –Easy to integrate with each other for visualization and other applications? 2

4. Semantics and Linked Data We need a way to describe the relationships between tabular data columns… Linked-data formats such as the Resource Description Framework (RDF) capture such relationships in subject- predicate-object triples. … and we need a method of description that is both standardized and machine-readable. Communities can develop, use, and reuse common vocabulary with ontologies, expressed in a computer- readable format: the Web Ontology Language (OWL) 3

5. Semantics and Linked Data Linked format aids interoperability, making it easier to share. Use existing URI’s to refer to well-defined entities and concepts: –How do you make sure that everyone using your data understands that the string “NY” refers to the US state of New York? –What more can you learn if you can easily discover other datasets that also refer to the US state of New York? 4

6. Use Case: SemantEco SemantEco is a data visualization environment that allows a user to explore ecological data through a map- based interface. Data comes from a variety of sources: –Federal, such as the USGS, EPA. –Local, such as the Darrin Freshwater Institute of Upstate New York. –… each with different notations and best- practices for gathering and recording. 5

7. Conceptually.... Represent data independent of the schema by which it was recorded This enables comparisons across data from different sources 6 In SemantEco, we look at Measurements: Water quality Air quality Birds Fish

8. SemantEco Annotator Allows a user to: Translate data into linked-data formats such as RDF: –Linked data triples describe how columns in a data table relate to each other, and to the data in that column. –OWL ontologies provide standard vocabularies for describing data these relationships. –Resulting enriched RDF data can be used immediately within RDF stores / hosted as LD. OR to utilize semantics to annotate data: –Column headers correspond to OWL properties –Data cell values can correspond to OWL classes or datatypes –Organizational best-practices and terminology can be defined in the data files themselves. 7

9. SemantEco Annotator: Getting Started 8

10. Provenance and Metadata 9 Annotator asks the user to provide metadata about the dataset. This is also becomes part of the final RDF, facilitating the dataset’s discoverability.

11. SemantEco Annotator 10 -- Tabular data view

12. SemantEco Annotator 11 -- Ontology loader -- Ontology facets

13. SemantEco Annotator 12 -- Global settings

14. SemantEco Annotator 13 -- Drag-and-drop to make assignments -- Work directly on tabular data

15. Ontologies 14 Load one or more ontologies from the dropdown menu. Or import from a URI. Annotator also maintains a list of recent imports for re-use.

16. Capabilities 15 Provide a definition for “Accession Code” Specify which standard was used to record the Date Group “Lake Name”, “Z Max” and “Sample Z” together as a single entity: the location where the sample was taken Make explicit that “NH4+” is the same thing as “Ammonium”, and that the units (mg/L) apply to each number in that column.

17. Integration with Semantic Applications 16 Identify application’s requirements: Eg., a piece of data with lat-long coordinates can be plotted on a map. We brought in data from the Darrin Freshwater Institute containing water quality data for lakes in Upstate New York, augmenting existing data from the U.S. Geological Survey. “Big Moose Lake”

18. Integration with Semantic Applications Linking data to well-defined entities and concepts by URI enhances searchability. 17 dbpedia: New_York “New York”“New York State” “NY” dbpedia: New_York_City

19. Future Work 18 Automatic mappings directed to a particular graph closed under a predicate/object pair, use of OWL domain and range restriction axioms to guide the user in vocabulary selection decisions Use of OWL class definitions to enable a top-down approach for modeling data Ability to load enhancement files, both to facilitate translation of multiple similar datasets, and to make corrections easier. Construction of a platform for better management of linked data, within which the Annotator plays a vital role. Use of application requirements to create “templates” for new data sources to be integrated more easily.

20. Summary 19 “SemantEco Annotator” component for ease of translation into RDF Multi-purposed for translation, annotation, and generalized mapping. A Part of a Future “Suite” that couples Annotation and Search

21. SemantEco Annotator Project Page Want more info? Interested in collaborating? See Evan Patton or email Deborah McGuinness dlm@cs.rpi.edu dlm@cs.rpi.edu We also have a project page with screenshots and demonstration videos: http://tw.rpi.edu/web/project/SemantEcoAnnotator 20

22. Acknowledgements Rensselaer Polytechnic Institute Tetherless World Constellation at RPI DataONE 21

23. SemantEco: More Info For additional information about SemantEco: “Addressing the Challenges of Multi-Domain Data Integration with the SemantEco Framework” Friday @ 10:35am, IN52B-02. E.W. Patton; P. Seyed; D.L. McGuinness 22