Panel:<br />Linked Data: Now What? <br />Noshir Contractor<br />Jane S. & William J. White Professor of Behavioral Science...
Mashing data <br />All social network data can be represented as file triples<br />Common framework to “mash” network data...
Querying data<br />Look for “structural signatures” – A is friends with B who is cited by C who is an expert on X<br />Use...
Reasoning about data<br />If C is an expert on X and Y is a sub-speciality of  X, then C is an expert on Y<br />
Gains for Social Network Research<br />Larger amounts of data<br />Higher resolution data<br />Longitudinal data<br />Mash...
Gains for Semantic Web community<br />Making recommendations motivated by social network theories<br />Making recommendati...
     Untested Conjecture		<br />Exposing Linked Data has gone past the Tipping Point.<br />But where are the applications ...
Semantic Web Integration Initial Test Bed<br />Semantic Web<br />Surveys<br />C-IKNOW<br />Text<br />mining<br />Web crawl...
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Noshir Contractor's view on the future of Linked Data

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Noshir Contractor's presentation for the ESWC 2010 Panel entitled "Linked Data: Now what?"

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  • Current configuration overview – our SNA tool based on Java and MySQLAdd ability to query the semantic web using standard query language, extract relevant data from the semantic web and make inferencesContribute to the semantic web by publishing our results as query-able online data or as an RDF snapshot
  • Noshir Contractor's view on the future of Linked Data

    1. 1. Panel:<br />Linked Data: Now What? <br />Noshir Contractor<br />Jane S. & William J. White Professor of Behavioral SciencesProfessor of Ind. Engg & Mgmt Sciences, McCormick School of Engineering <br />Professor of Communication Studies, School of Communication & <br />Professor of Management & Organizations, Kellogg School of Management,<br />Director, Science of Networks in Communities (SONIC) Research Laboratory<br />nosh@northwestern.edu <br /> Supported by NSF : OCI-0753047, IIS-0729505, IIS-0535214, SBE-0555115<br />  <br />SONIC<br />Advancing the Science of Networks in Communities<br />
    2. 2. Mashing data <br />All social network data can be represented as file triples<br />Common framework to “mash” network data from diverse sources<br />Performance challenges (circa 2005)<br />
    3. 3. Querying data<br />Look for “structural signatures” – A is friends with B who is cited by C who is an expert on X<br />Use “structural signatures” to make recommendations. Recommend that A contact C because …..<br />Performance challenges<br />
    4. 4. Reasoning about data<br />If C is an expert on X and Y is a sub-speciality of X, then C is an expert on Y<br />
    5. 5. Gains for Social Network Research<br />Larger amounts of data<br />Higher resolution data<br />Longitudinal data<br />Mashing data<br />Querying data<br />Reasoning and inference over data<br />
    6. 6. Gains for Semantic Web community<br />Making recommendations motivated by social network theories<br />Making recommendations motivated by social network analytic methods<br />Identification of killer applications <br />
    7. 7. Untested Conjecture <br />Exposing Linked Data has gone past the Tipping Point.<br />But where are the applications using Linked Data? <br />While Linked Data is proliferating, the killer application will require the addition of the “social” into the data<br />The case of PopSciGrid:<br />Analyzing same variables across diverse data sources <br />Recommending workflow, datasets, documents and people<br />
    8. 8. Semantic Web Integration Initial Test Bed<br />Semantic Web<br />Surveys<br />C-IKNOW<br />Text<br />mining<br />Web crawling<br />JAVA<br />SPARQL<br />Publish<br />model<br />SPARQL<br />Inference engine<br />Databases<br />(e.g. JENA OWL)<br />Activity<br />logs<br />(e.g. <br />D2R<br />Virtuoso)<br />Relational-to-RDF Server<br />MySQL<br />SONIC<br />Advancing the Science of Networks in Communities<br />Suggestions welcome!<br />

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