This talk introduces Linked Data and Semantic Web by using two examples - population sciences grid and semantAqua - a semantically enabled environmental monitoring. It shows a few tools and the semantic methodology and opens discussion for LOD and team science

1. Linked Open Data as an Enabler for
Team 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
Science of Team Science; LOD and Team Science April 19, 2012

2. Background
– 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, …

3. 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

4. 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
4

5. Semantic Web Perspective on
Initial PopSciGrid 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) impact 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? 5

6. Foundations: The Tetherless World
Constellation Linked Open Government
Data Portal
Convert TWC LOGD
Query/
Access
LOGD Community Portal
SPARQL • RDF
Endpoint • RSS
• JSON
Create • XML
• HTML
• CSV
•…
Enhance
Data.gov deployment
6

7. What is an Ontology?
Thesauri
“narrower Formal Frames General
Catalog/ term” is-a (properties) Logical
ID relation constraints
Informal Formal Value Disjointness
Terms/ instance Restrs. , Inverse,
glossary is-a 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

8. 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?
(Mobile)
Knowledge Intelligent
Provenance in Virtual
Agents NSF Interops:
Observatories SONET
SSIII – Sea Ice
Intelligence Analyst
Tools
Hypothesis
Investigation /
Policy Advisors
8

9. Foundations: Web Layer Cake
Visualization APIs
S2S
Govt Data
Inference Web, Proof
Markup Language, W3C Inference Web IW Trust,
Provenance Working Air + Trust
group formal model,
W3C incubator group, DL, KIF, CL, N3Logic
…
Ontology repositories
OWL 1 & 2 WG Edited main OWL (ontolinguag),
Docs, quick reference, Ontology Evolution env:
OWL profiles (OWL RL), Chimaera,
Earlier languages: DAML, Semantic eScience
DAML+OIL, Classic Ontologies, MANY other ontologie
RIF WG
AIR accountability tool
SPARQL WG, earlier QL –
OWL-QL, Classic’ QL, …
Govt metadata search
Linked Open Govt Data
SPARQL to Xquery translator RDFS materialization
(Billion triple winner) Transparent Accountable
Datamining Initiative (TAM

11. PopSciGrid Workflow
Ban coverage
Publish
CSV2RDF4LOD
Direct visualize
derive derive
CHSI 2009
archive
Archive
SemDiff
CSV2RDF4LOD
derive
Enhance

12. PopSciGrid Example
State -Hawaii
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
12
 Feedback users’ comment to gov contact (e.g. %)
 Annotation capabilities coming (and more)

13. PopSciGrid II

14. 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 – but not at
scale 14

15. Updates and Motivations from a
Computer Science Perspective
Old: New:
• Raw conversions • Enhanced conversions
• Per-dataset vocabularies • Vocabulary reuse
• Custom queries • Generic queries
• Custom data • Re-usable data
management code management code
• Limited use because of • Unlimited use of new
Google Visualization open source visualization
licenses toolkit
• State-level data • State and county-level
data
15

16. RDF Data Cube
Vocabulary
• Integrated with the LOGD
• For publishing multi- data conversion
dimensional data, such infrastructure
as statistics, on the web
in such a way that it can • Integrated with other tooling
be linked to related data like Stats2RDF
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
16

17. County
average life
expectancy
(Summary Measures of Health

18. SemantEco/SemantAqua
• Enable/Empower citizens &
scientists to explore pollution
sites, facilities, regulations, and
health impacts along with
provenance. 5 4
• Demonstrates semantic 2 3
monitoring possibilities.
• Map presentation of analysis
• Explanations and Provenance 1
available
http://was.tw.rpi.edu/swqp/map.html and
1. Map view of analyzed results http://aquarius.tw.rpi.edu/projects/semantaqua
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

19. System Architecture
Virtuoso
access
19

20. 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

21. Discussion
• Semantic Technologies and Linked Data are
powering a wide array of application – many
in Big Science, Team Science, at least
interdisciplinary science
• Labeled graphs as powered by structured
data can be a nice corpus for evaluation
• Tools and methodologies are ready for use
• We love to partner in these areas
• What do you need or want from linked data?
Questions? - dlm @ cs . rpi . edu

22. Extra

23. Directions
• 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
23

24. Enabling Subject Area Exploration
and Hypothesis Generation
• What factors influence prevalence (and under what conditions)?
• Within smoking, should we focus on prevalence, packs sold,
quit rate, hospital admission diagnosis, other?
• What is prevalence (definition)? And how is it measured (overall
/ in this data set)?
• What are the conditions under which the data was obtained
(date, sample set, extenuating conditions, …)
• What other data might we include? And how might we show
that data?
• What should be represented ? And how should it be
manipulated?
• What tools and services to people benefit from to explore?
Encode? Act?

25. Semantically-enabled advisors
utilize:
• Ontologies
• Reasoning
• Social
• Mobile
• Provenance
• Context
Patton & McGuinness.et. al
tw.rpi.edu/web/project/Wineagent

26. Semantic
Sommelier
Previous versions used ontologies
to infer descriptions of wines for
meals and query for wines
New version uses
Context: GPS location, local
restaurants and wine lists, user
preferences
Social input: Twitter, Facebook, Wiki,
mobile, …
Source variability in quality,
contradictions exist,
Maintenance is an issue… however
new models emerging

27. • Semantic Technologies: ready for use
•
The Semantic Web
Tools & tutorials available; deep apps
enables…
future planning may benefit from
consultants
• • New models of intelligent services
Context-aware, semantic
apps are the future
• E-commerce solutions
• M-commerce
• Web assistants
• …
New forms of web assistants/agents that act on a
human’s behalf requiring less from humans
and their communication devices…