Linking Disparate Datasets of the Earth Sciences with the

SemantEco AnnotatorSession: Managing Ecological Data for

Effective Use and Reuse

Patrice Seyed1,2, Katherine Chastain1, and Deborah McGuinness11 Tetherless World Constellation, Rensselaer Polytechnic Institute, 110 8 th Street, Troy, NY 121802 DataONE, University of New Mexico, 1 University Boulevard N.E., Albuquerque, NM 87131

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

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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 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)

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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 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.

Conceptually....

• Represent data independent of the schema by which it was recorded

• This enables comparisons across data from different sources

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• In SemantEco, we look at Measurements:• Water quality• Air quality• Birds• Fish

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.8

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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.

• 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”

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Integration with Semantic Applications

• Linking data to well-defined entities and concepts by URI enhances searchability.

dbpedia:New_York

“New York”“New York State”“NY”

dbpedia:New_York_City

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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

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