1. Ontology Collaboration Tools and Services
Federation Of Earth Science Information Partners (ESIP)
Summer Meeting, July 22, 2010
Knoxville, TN
Bruce Bargmeyer
Lawrence Berkeley National Laboratory
Tel:

2. Topics Eye on Earth Summit, Abu Dhabi
Technology Infrastructure Workgroup
Technology vision for EOE Summit/UNEP
Ontology and voiD tools
Some practical examples

3. Technical Infrastructure
An Eye on Earth Summit will be held in Abu Dhabi in December (Possibly March – May 2011)
Ecoinformatics International Technical Collaboration is involved in planning and content development
A Technical Infrastructure Working Group will “focus on the technical components of the environmental information federation frameworks, addressing information and communications technology interoperability, connectivity, data standards, data format and content standards, and other such issues. This includes standards for the capture, description, and structuring of scientific data, and the development and delivery of various products and services.”

4. W3C, Web 2.0, Web 3.0 View
Suppose Sir TBL gave a presentation at the EOE Summit – Likely topic:
Linked Data (aka Linked Open Data) – spirited inspirational presentation like he recently gave at TED and Gov 2.0 conferences.
Publishing Linked Data involves 3 basic steps
1. Assign URIs to the entities described by the data set and provide for dereferencing these URIs over the HTTP protocol into RDF representations.
2. Set RDF links to other data sources on the Web, so that clients can navigate the Web of Data as a whole by following RDF links.
3. Provide metadata about published data, so that clients can assess the quality of published data and choose between different means of access.
Other Topics …

5. How to Do That …
Suppose the United Nations Environmental Program wants to take such an approach to developing virtual state of the environment reports for countries (or other areas) around the world.
What technology infrastructure could help?
Essential components
Data (preferably in RDF)
Vocabulary, e.g., ontology
Metadata, e.g., Vocabulary of Interlinked Datasets (voiD)

6. Building Shared Vocabularies
Moving from Limited Semantics to Fuller Semantics
fuller semantics
Formal Ontology
Concepts and relations expressed in an ontology representation language that provides formal semantics (i.e., specifies logical inferences).
OWL, Common Logic
Increasing semantics expressivity
Conceptual Model
ER/UML
Concepts and relations among them in a modeling language
Taxonomy/Thesaurus
Terms (possibly with definitions) & relations between terms
Glossary
Terms associated with definitions (concepts)
Keywords
Terms
limited semantics

7. Connection between imagery and ontology
Semiconductor Facility
Pipe
Water Treatment
Facility
Vehicle Parking/Storage
Process Building
Air Handling
Facility
Process Building
Roof
Warehouse
Tank
Enclosed Passageway
Administration Building
Process Building
Vehicle Parking/
Storage
Gas
Utilities
Facility
Wall
Flare
Flag pole
Vehicle Storage/Parking
Road
Fence
Lawn

8. Connection between imagery and ontology
Does A=B? If True, imaged object is modeled entity.
Road
Pipe
Tank
Flare
Flagpole
Roof
Process Building
Administration Building
Warehouse
Vehicle Storage/Parking
Enclosed Passageway
Fence
Wall
Lawn
Semiconductor Facility
Air Handling Facility
Water Treatment Facility
Gas Utilities Facility
route
Road
Pipe
Tank
instrument
Flare
hasVALUE = B
Flag pole
Roof
covering
Process Building
Administration Building
Warehouse
building
Vehicle Storage/Parking
Enclosed Passageway
Fence
Wall
barrier
Lawn
Semiconductor Facility
Air Handling
Facility
hasVALUE = A
land cover/land use
Water Treatment
Facility
processing facilities
Gas
Utilities
Facility

9. Ontology Collaboration Tool Capabilities and Services
Register ontologies for use and development – upload & download
Browse ontologies to find concepts, terms, definitions
Visualize ontology neighborhoods & hierarchy to root
Display metadata describing each ontology
Calculate metrics – classes, depth, definitions, structure, …
Comment on concepts, relations, definitions, things to add, gaps…
Map between concepts in different ontologies
Create views of ontologies that highlight important content
Provide an interface to query and download information, e.g., SPARQL endpoint
Link ontology terms to resources (e.g., simulations, algorithms, models)

10. Ontology Collaboration Tool Services
Find and download selected ontologies
Answer questions about concepts/terms:
Find terms in one or more ontologies.
What is the definition?
What is the preferred term for a concept?
What other concepts/terms are related?
Find resources (data, simulations, algorithms, models) through ontology mediated search
Annotate text using ontology terms – direct, mapped, or through inference.
…. (movie)

11. Browse Ontologies

12. Ontology Metadata

13. Ontology Metrics

14. Search for terms in Multiple Ontologies

15. View Concept Definition and Details

16. Visualize Ontology Neighborhood

17. Write Comments, Notes and Reviews … Subscribe to any Changes in o\Ontologies, Concepts, etc.
Participants can use the “Notes” and “Review” functionality to:
• Comment on classes, relations, definitions, gaps, …
Provide feedback to ontology authors
• Reach consensus on ontology decisions
• Review ontologies and their components
Subscribe to be notified of updates

18. Make Mappings between Ontologies

19. Projects Sign-up for Ontologies

20. Annotate Text with Ontology Terms
Direct Annotations
Extended annotations
generated from the ontology
is_a transitive closure.
Extended annotations
generated from the ontology
is_a transitive closure.

21. Ontology Enabled Resource Discovery
Data, models, simulations, …

22. Metadata for LOD
“In order to support clients in choosing the most efficient way to access Web data for the specific task they have to perform, data publishers can provide additional technical metadata about their data set and its interlinkage relationships with other data sets …. The Vocabulary Of Interlinked Datasets … defines terms and best practices to categorize and provide statistical metainformation about data sets as well as the linksets connecting them.”
-- Tim Berners-Lee, Massachusetts Institute of Technology, et al
voiD is a vocabulary and a set of instructions that enables the discovery and usage of linked datasets. A dataset is a collection of data, published and maintained by a single provider, available as RDF, and accessible, for example, through dereferenceable HTTP URIs or a SPARQL endpoint. Based on the voiD vocabulary this document explains how to use voiD in a practical setup, for both data consumers and data providers. -- from voiD Guide

23. voiD is Extensible The voiD vocabulary is extensible.
It may be useful to extend it as needed for evaluating and documenting data for environmental decision making.
E.g., EPA data standards, ISO/IEC data descriptions

24. A Practical Example Provided by Pasky Pascual, EPA
Inspired by his article: “Evidence-based decisions for the wiki world”, Pasky Pascual, International Journal of Metadata, Semantics and Ontologies (IJMSO) Volume 4 - Issue 4 – 2009 DOI: /IJMSO
He provided data for the Gulf of Maine
LBNL is using this to demonstrate environmental linked data and voiD files.
voiDer software creates ISO/IEC type metadata on the dataset. This can be used to transform data into RDF and can be transcribed into voiD descriptions.

25. Preserving the Comparability of Sensor Data
A Possible Use Case
Charles S. Spooner, US EPA
ESIP 2010 Winter Meeting
Washington, DC

26. Water Quality Data Overwhelmingly an investment by public agencies
Our goal is to preserve that investment for future use recognizing that:
the value of good data increases over time
the value of undocumented data decreases quickly

27. Proposed Use Case Compare metadata fields for
Existing WQ sensors in different settings
Vertical profilers
Data Flow systems
Autonomous Underwater Vehicles
The WQX Schema
Sensor Workgroup Data Elements
AQ, Calibration, Operator Competence
Water ML
Sensor ML

28. Use Case Results
Source: Charles Spooner, ESIP Water Cluster Presentation, January 2010

29. Toxicity Data for MA, ME, NH

30. Fit into W5H Observations are Events Who: collecting agency
07/21/10
Fit into W5H
Observations are Events
Who: collecting agency
What: observable measured
When, Where
How: method/protocol
use rdf:value for measurement

31. voiDer - Creation of ISO/IEC 11179 Metadata From Gulf of Main Toxicity Data Files (.xls)
Results from a “clean” file:

32. 07/21/10
voiDer - creation of voiD files From Gulf of Main Toxicity Data Files (.xls)
Results from a “clean” file:
Where
When
What
Value

33. voiDer - Creation of ISO/IEC 11179 Metadata From Gulf of Main Toxicity Data Files (.xls)
Results from a “messy” file:

34. 07/21/10
voiDer - creation of voiD files From Gulf of Main Toxicity Data Files (.xls)
Shared ontology:
@base < .
@prefix obs: < .
<> an owl:Ontology; owl:imports < .
:Toxicity a owl:Class; owl:subClassOf obs:Observation .
:species a owl:ObjectProperty; rdf:domain :Toxicity;
rdf:range < .
...
RDF data:
@prefix tox: < .
<> an owl:Ontology; owl:imports < .
<#Place-1> a geo:Point ; geo:lat 43.1 ; geo:long ; rdf:label “Spinney Creek” .
<#_2> a tox:Toxicity ; w5h:where <#Place-1> ; w5h:when “ ”^^xsd:Date ;
tox:Species ncbi:Mytilus ; rdf:Value -58 .

35. Practical Example: Ontology + Metadata in Use SciScope
SciScope shows the use of a water ontology, linked to water “variables” (data elements), with metadata that describes the data, and an easy to use geographic interface
Demonstrates capabilities that help users to discover, evaluate, and access water data from millions of sensors for analysis, presentation, assessment, ….
Shows use of metadata to describe the data
Developed in collaborative effort between Microsoft Research, Berkeley Water Center (UCB), Lawrence Berkeley National Laboratory
Use it at: SciScope.org (Hosted by LBNL)
See a movie: SciScope_Movie.wmv

36. SciScope – Ontology & Metadata put millions of sensors at your fingertips
descriptions
Ontology
mediated
discovery
Browse geographical features from eco-regions and hydrology to geology
SciScope facilitates data discovery from 9.5 million sensors in the USA operated by agencies such as USGS, EPA and NOAA offering observation results from late 1800’s to the current day
Find and retrieve historic and near real-time data about the environment from multiple databases
Assemble data from multiple heterogeneous databases with ease
Adapted from source: Bora Beran, Microsoft Research

37. What is behind SciScope?
Knowledge Base
Relationships are stored as RDF triples in a relational database
‘Escherichia coli’ = ‘E. coli’
‘E. coli’ is-a ‘Indicator Organism’
‘Nitrogen’ is-a ‘Macronutrient’
‘Macronutrient’ is-a ‘Nutrient’
‘Hypoxia’ isMeasuredUsing ‘DissolvedOxygen’
‘Hypoxia’ isRelatedTo ‘Eutrophication’
Supports transitive, symmetric and inverse properties
Inferred statements are pre-computed
Source: Bora Beran, Microsoft Research

38. Inference In SciScope Transitive ‘Nitrogen’ is-a ‘Macronutrient’
‘Macronutrient’ is-a ‘Nutrient’
Inference: ‘Nitrogen’ is-a ‘Nutrient’
Symmetric
‘Hypoxia’ isRelatedTo ‘Eutrophication’
Inference: ‘Eutrophication’ isRelatedTo ‘Hypoxia’
Inverse
Inference: ‘Nutrient’ isBroaderThan ‘Macronutrient’
Source: Bora Beran

39. What is Behind SciScope – Linking Concept Systems to Data

40. What is behind SciScope?
Geographical Features Catalog
Collection of features such as dams, aquifers, geologic formations, watersheds, sensors
Based on data and maps from USGS, EPA, National Atlas
Source: Bora Beran

41. Show SciScope Movie

42. Acknowledgements Bora Beran, MicroSoft Research Kevin Keck, LBNL
Glenn May, LLNL
Mark Musen, Natasha Noy, et al, Stanford
Pasky Pascual, EPA
Charles Spooner, EPA
This material is based upon work supported by the National Science Foundation, under Grant No , by USEPA and by DOE. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, DOE, or USEPA .