Gt health2stat 7-22-2010
Upcoming SlideShare
Loading in...5
×

Like this? Share it with your network

Share

Gt health2stat 7-22-2010

  • 1,474 views
Uploaded on

 

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
1,474
On Slideshare
1,331
From Embeds
143
Number of Embeds
3

Actions

Shares
Downloads
4
Comments
0
Likes
1

Embeds 143

http://www.dagoneye.it 141
http://georgethomasname.blogspot.com 1
http://www.linkedin.com 1

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. Data.gov: Web, Data Web, Social Data Web [email_address] 7/22/2010 #health2stat
  • 2.
      • The Web is evolving…
        • From a Web of Linked Documents,
        • To a Web of Linked Data!
  • 3.
      • OK…
      • What does that mean?
      • Bear with me for a quick overview -
  • 4.
      • There’s only one type of link in HTML
        • SourcePage.htm contains markup;
          • <a href='target.html'> hyperlink </a>
  • 5.
      • XML gave us custom tags
      • <myTag> typed data </myTag>
      • Good, but more importantly...
  • 6.
      • The Architecture of the Web
      • (one of the most important and successful inventions of the 20 th Century!)
      • is REST ful
      • (Info) Resource Representations (URI’s vs URL’s), Uniform Interface (CRUD via HTTP methods), server Resource and client App state (via links)...
  • 7.
      • Web + Data = Data Web
      • The Data Web (aka Web of Data) is an innovation that extends the existing Web of Documents
      • Beginning with the Resource Description Framework ( RDF )
      • Which is a Web based data modeling language
  • 8.
      • RDF gives us custom link types :
      • node <arc> node
      • thing <relationship> thing
      • topic <property> topic
      • entity <attribute> value
      • subject <predicate> object
      • the grammar of ‘triples’
  • 9.
      • HTTP GET ’dereferences’ RDF triples in multiple serialization (.htm, .rdf, .json) formats
      • So, the URI
      • http://agency.gov/Subject
      • has a URL representation that returns
      • <http://my.org/predicate> <http://your.com/Object>
  • 10.
      • Grammar needs vocabularies ...
      • Vocabularies can be metadata :
      • Objective quantifies Goal
      • So http://standards.org/vocab/Objective returns;
      • <quantifies> <http://standards.org/vocab/Goal>
  • 11.
      • Instance data (re)uses vocabularies:
      • Meaningful Use 'is a' (type of) Objective
      • http://onc.hhs.gov/Meaningful_Use
      • <type> <http://standards.org/Objective>
      • EHR Exchange 'is a' (type of) Goal
      • http://onc.hhs.gov/EHR_Exchange
      • <type> <http://standards.org/Goal>
  • 12.
      • OK…
      • Now we’ve introduced to the what , but -
      • WHY should we do this Linked (Open Gov) Data stuff?
  • 13.
      • We can (automatically) infer things like;
      • Meaningful Use quantifies EHR Exchange
      • We can traverse (via apps and browsers) the data graph with no apriori domain knowledge …
      • We/others just make links to correlate disparately owned/managed/published data across distinct (some say federated) domains…
  • 14.
      • We can (automatically) integrate disparate data sites/sources via graph merging !
      • If http://hhs.gov/about returns;
      • <owl:sameAs> <http:// dbpedia.org /DHHS>, <http://www. freebase.com /HHS> .
      • All these data from each site is seen as one dataset, substantially lowering coordination costs of integration!
  • 15.
      • Who is doing this in Industry?
      • Google, Facebook, Yahoo
      • NY Times, Newsweek
      • Best Buy, …
      • What about Government Agencies?
      • Library of Congress Subject Headings
      • http://id.loc.gov – pace setting example!
  • 16.
      • So what about Data.gov?
      • (Homepage: ‘Linking Open Government Data’)
      • Data.gov PMO SemWeb and Linked Data Team
      • Collaboration with tw.rpi.edu – SemWeb inventors
      • Federal CIOC-AIC Data Architecture Subcommittee Open Government Vocabulary WG
  • 17.
      • How about at HHS?
      • NLM: UMLS ‘SPARQL endpoint’
      • http://mor.nlm.nih.gov/sparql
      • CMS: Data.gov PMO SemWeb Team members
      • CMS Dashboard vocabulary WIP
      • CHDI: vocabularies and URI schemes
  • 18.
      • Rich linking scenarios to explore;
      • What are successful community intervention tactics to combat childhood obesity?
      • For these geographic health stats, what evidence based provider payment/performance trends emerge?
      • What other Gov data is relevant? Is there something in the air or water?
  • 19.
      • Web + Data = Data Web;
      • Data Web + Social = Social Data Web!
      • Consider the metadata creation (domain SME’s) and instance data curation (info workers) as objects of social collaboration.
      • Activity-stream history feeds, viral expert networking (etc.) will all contribute to enhanced data quality .
      • Think structured data wikis, where tags are suggested and come from SME designed ontologies (vocabularies) instead of user-generated folksonomies.
  • 20.
      • What can YOU do?
      • Get involved with the Data.gov PMO and/or the FCIOC-AIC-DAS-OGV-WG!
      • (Come to DC Semantic Web meetups too...)
      • Contact me 
      • THANKS!!!