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Linked Data and Semantic Technologies can support a next generation of science. This talk shows examples of discovery, access, integration, analysis, and shows directions towards prediction and vision.
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Linked Open Data and Next
Generation Science
Deborah L. McGuinness Tetherless World Senior Constellation Chair
Professor of Computer and Cognitive Science
Rensselaer Polytechnic Institute, Troy, NY
& CEO McGuinness Associates, Latham, NY
Earth System Information Partners, Madison Wisconsin, July 18, 2012
Background I
– Access to data is exploding with open government
data and numerous agencies publishing and
providing services access or at least FOIA access
– Citizen interest and contributions are increasing –
data gathering (e.g., bird observations), reviewing
(e.g., galaxy zoo), compute cycles (e.g., SETI), …
– Arguably the more large (both data volume and area
breadth) science problems need addressing – these
go beyond what a single research team can easily
solve
Background II
– Semantic Technologies – technological support for
encoding meaning in a form computers can
understand and manipulate – are maturing and
increasing in usage
– Computational encodings of meaning can be used
to help integrate, link, validate, filter,…. Essentially
to make smarter, more context-aware applications
– Semantic Technologies enable linking data … and
linked data provides a way of connecting and
traversing information, nodes, graphs, webs, …
Take Home Message
(early) – Linked Data is usable now by any project
– Linked Data and Semantic Technologies can help in
forming and connecting help large, distributed,
evolving efforts such as many earth and space
science projects
– In the rest of talk:
– Brief intro to Linked Data and Semantic
Technologies through examples
– Discussion about what we might do now and strive
for in the future
Linked Data
• Linked Data is quite simple and follows principles set
out by Berners-Lee in
http://www.w3.org/DesignIssues/LinkedData.html
– Use URIs as names for things
– Use HTTP URIs so that people can look up those names.
– When someone looks up a URI, provide useful information,
using the standards (RDF*, SPARQL)
– Include links to other URIs. so that they can discover more
things.
– Introduction by examples and then discussion
Population Sciences Grid Goals
• Convey complex health-related information to
consumer and public health decision makers
for community health impact
• Inform the development of future research
opportunities effectively utilizing
cyberinfrastructure for cancer prevention and
control
McGuinness, D. Shaikh, A., Lebo, T, Ding, L., Courtney, P., McCusker, J., Moser,. Morgan, G.D., Tatalovich, Z., Willis, G., Contractor, N., and Hesse, B.
2012. Towards Semantically-Enabled Next Generation Community Health Information Portals: The PopSciGrid Pilot In Proceedings of Hawaii
International Conference on System Sciences 2012
6
Semantic Web Perspective on
Initial Project Goals
• How can semantic technologies be used to integrate, present,
and analyze data for a wide range of users?
• Can tools allow lay people to build their own demos and
support public usage and accurate interpretation?
• How do we facilitate collaboration and “viral” applications?
• Within PopSciGrid:
– Which policies (taxation, smoking bans, etc) are correlated with health
and health care costs?
– What data should be displayed to help scientists and lay people
evaluate related questions?
– What data might be presented so that people choose to make (positive)
behavior changes?
– What does the data show? why should someone believe that?
– What are appropriate follow up questions to support actionability?
7
What is an Ontology?
Catalog/
ID
General
Logical
constraints
Terms/
glossary
Thesauri
“narrower
term”
relation
Formal
is-a
Frames
(properties)
Informal
is-a
Formal
instance Value
Restrs.
Disjointness
, Inverse,
part-of…
Ontologies Come of Age McGuinness, 2001, and From AAAI Panel 99 – McGuinness, Welty, Uschold,
Gruninger, Lehmann
Plus basis of Ontologies Come of Age – McGuinness, 2003
Inference Web: Making Data Transparent and
Actionable Using Semantic Technologies
• How and when does it make sense to use smart system results & how do we
interact with them?
9
Knowledge
Provenance in Virtual
Observatories
9
Hypothesis
Investigation /
Policy Advisors
(Mobile)
Intelligent
Agents
Intelligence Analyst
Tools
NSF Interops:
SONET
SSIII – Sea Ice
SPARQL to Xquery translator RDFS materialization
(Billion triple winner)
Govt metadata search
Linked Open Govt Data
SPARQL WG, earlier QL –
OWL-QL, Classic’ QL, …
OWL 1 & 2 WG Edited main OWL
Docs, quick reference,
OWL profiles (OWL RL),
Earlier languages: DAML,
DAML+OIL, Classic
RIF WG
AIR accountability tool
DL, KIF, CL, N3Logic
Inference Web, Proof
Markup Language, W3C
Provenance Working
group formal model,
W3C incubator group,
…
Inference Web IW Trust,
Air + Trust
Visualization APIs
S2S
Govt Data
Ontology repositories
(ontolinguag),
Ontology Evolution env:
Chimaera,
Semantic eScience
Ontologies, MANY other ontologies
Transparent Accountable
Datamining Initiative (TAMI)
Foundations: Web Layer Cake
PopSciGrid Workflow
CSV2RDF4LOD
Direct
SemDiff
Archive
CSV2RDF4LOD Enhance
visualize
derive derive
derive
arc
hiv
e
Publish Ban coverage
CHSI 2009
PopSciGrid Example
State View
13
Extensible Mashups via Linked Data
Diverse datasets from NIH
Potentially linking to other content (e.g.
“unemployment rate”)
Accountable Mashups via Provenance
Annotate datasets used in demos
Feedback users’ comment to gov contact (e.g. %)
Annotation capabilities coming (and more)
PopSciGrid II
Reflections
Successful but….
• What if we could allow data experts to build
their own demos?
• What if we could allow non-subject matter
experts to function as subject-literate staff?
• What if team members could interchange roles
(and thus make contributions in other areas)?
• What technological infrastructure is required?
• Claim: all of this is being done now – and it is
starting to scale and growing more accessible 15
Updates and Motivations from a
Computer Science Perspective
Old:
• Raw conversions
• Per-dataset vocabularies
• Custom queries
• Custom data
management code
• Limited use because of
Google Visualization
licenses
• State-level data
New:
• Enhanced conversions
• Vocabulary reuse
• Generic queries
• Re-usable data
management code
• Unlimited use of new
open source visualization
toolkit
• State and county-level
data 16
County
average life
expectancy (Summary Measures of Health)
Why Did I Show A Population Science
Project and a Water Project?
Questions and goals are similar –
What’s happening with x? – health of a country,
water quality and other parts of an ecosystem,
climate changes
What intervention strategies are being tested
What policies are correlated with factors under
investigation
And
Why should people believe the outcome?
See Global Change Provenance Representation in the
Global Change Information System (GCIS)
Curt.Tilmes@nasa.gov
What’s happening with the climate
and how will it affect the U.S.?
National Climate Assessment 2013
30 chapters, 240 authors
A “Highly Influential Scientific Assessment”
Why should I believe it?
GCIS presenting the provenance of the report
itself, the key messages of the report,
including traceable accounts of the >500
technical inputs from reports, papers, models,
datasets, observations, etc.
SemantEco/SemantAqua
• Enable/Empower citizens &
scientists to explore pollution
sites, facilities, regulations, and
health impacts along with
provenance.
• Demonstrates semantic
monitoring possibilities.
• Map presentation of analysis
• Explanations and Provenance
available
1
2 3
http://was.tw.rpi.edu/swqp/map.html and
http://aquarius.tw.rpi.edu/projects/semantaqua
4 5
1. Map view of analyzed results
2. Explanation of pollution
3. Possible health effect of contaminant (from EPA)
4. Filtering by facet to select type of data
5. Link for reporting problems
6. Now joint with USGS resource managers ; expanded to
endangered species; now more virtual observatory style
System Architecture
access
Virtuoso
21
Originally developed for VSTO, now in SSIII, SESDI, SESF, OOI …
The Virtual Solar-Terrestrial
Observatory: A Deployed Semantic Web Application Case Study for Scientific Research. Proc. 19
Conf. on Innovative Applications of Artificial Intelligence (IAAI-07),
http://www.vsto.org
Reflections
• What began as Semantic water quality monitoring is now SemantEco –
ecological and environmental monitoring in support of ecosystem analysis
• Now includes endangered species and related health impacts working with
USGS to prototype resource manager dashboard
• Expanding to include citizen science reporting on water on mobile platforms
• Now working with SONet, Santa Barbara County LTER, CUASHI to integrate
other related scientific observations
– Current focus use case ecological researcher
– Find relevant data (within and outside DataOne) by region, timeframe,
chemical, measurement dimension, species
– Currently background ontology is relatively simple and aims more at
discovery and integration
• Semantic Sea Ice project aimed at helping arctic ice researchers find and
evaluate data in support of understanding the state of ice in the arctic
• These technologies span the spectrum of supporting discovery, integration,
analysis, and ultimately prediction 23
Discussion
• Semantic Technologies and Linked Data are powering a wide array of applications – many in Big Science, Team Science, at least interdisciplinary science
• Tools and methodologies are ready for use
• We love to partner in these areas
• What do you need or want from linked data and semantic technologies?
Questions? - Deborah McGuinness
dlm @ cs . rpi . edu
Extra
RDF Data Cube
Vocabulary
• For publishing multi-dimensional data, such as statistics, on the web in such a way that it can be linked to related data sets and concepts using RDF.
• Compatible with the cube model that underlies SDMX (Statistical Data and Metadata eXchange).
• Also compatible with: – SKOS, SCOVO, VoiD,
FOAF, Dublin Core Terms
• Integrated with the LOGD
data conversion
infrastructure
• Integrated with other tooling
like Stats2RDF
26
Foundations: The Tetherless World
Constellation Linked Open Government
Data Portal
27
Create
TWC LOGD
Convert
Query/
Access
LOGD
SPARQL
Endpoint
Enhance
• RDF
• RSS
• JSON
• XML
• HTML
• CSV
• …
Community Portal
Data.gov deployment
Directions
28
• Incorporation of TWC data Quality Facts label (Zednik et al)
• Use of DataFAQs automated data quality framework (Lebo et al)
• Additional provenance inclusion / usage (Inference / Provenance Web)
• Annotation / Collaboration facilities (Michaelis et al)
• Other data sets? Or exposition of other parameters?
• Partners in additional topic areas
Recommended