Linking Disparate Datasets of the
Earth Sciences with the SemantEco
Annotator
Session: Managing Ecological Data for
Effective Use and Reuse
Patrice Seyed1,2, Katherine Chastain1, and Deborah McGuinness1
1
Tetherless World Constellation, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY 12180
2 DataONE, University of New Mexico, 1 University Boulevard N.E., Albuquerque, NM 87131
Overview
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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
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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?
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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 subjectpredicate-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 computerreadable format: the Web Ontology Language (OWL)
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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?
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Use Case: SemantEco
• SemantEco is a data visualization
environment that allows a user to
explore ecological data through a mapbased 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 bestpractices for gathering and recording.
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Conceptually....
• Represent data independent of the
schema by which it was recorded
• This enables comparisons across
data from different sources
• In SemantEco,
we look at
Measurements:
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Water quality
Air quality
Birds
Fish
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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.
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SemantEco Annotator:
Getting Started
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Provenance and Metadata
• Annotator asks the user to provide
metadata about the dataset.
• This is also becomes part of the final RDF,
facilitating the dataset’s discoverability.
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SemantEco Annotator
-- Tabular data view
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SemantEco Annotator
-- Ontology loader
-- Ontology facets
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SemantEco Annotator
-- Global settings
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SemantEco Annotator
-- Drag-and-drop to make assignments
-- Work directly on tabular data
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Ontologies
• 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.
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Capabilities
• 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.
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Integration with Semantic
Applications
• Identify application’s
requirements:
• Eg., a piece of data with lat-long
coordinates can be plotted on a map.
“Big Moose
Lake”
• 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.
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Integration with Semantic
Applications
• Linking data to well-defined entities and
concepts by URI enhances searchability.
dbpedia:
New_York
“NY”
“New York State”
dbpedia:
New_York_City
“New York”
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Future Work
• 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.
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Summary
• “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
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SemantEco Annotator
Project Page
Want more info? Interested in collaborating?
See Evan Patton or email Deborah McGuinness
dlm@cs.rpi.edu
We also have a project page with screenshots
and demonstration videos:
http://tw.rpi.edu/web/project/SemantEcoAnnotator
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Acknowledgements
• Rensselaer Polytechnic Institute
• Tetherless World Constellation at RPI
• DataONE
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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
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