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SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
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SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics

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Semantic Web Applications in Scientific Discourse Workshop at the International Semantic Web Conference / Washington, DC / 26th October 2009

Semantic Web Applications in Scientific Discourse Workshop at the International Semantic Web Conference / Washington, DC / 26th October 2009

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  • [Steve - another slide best shown in animation - I have broken it into two slides to make it more readable on printout.] As we said in the previous slide, SWAN’s content is specialized scientific content presented in a way scientists can use. <click to get first animation - slide fades and scientist view is circled>.
  • Transcript

    • 1. SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics Alexandre Passant 1 , Paolo Ciccarese 2, 3 , John G. Breslin 4 , Tim Clark 2, 3 1 DERI, NUI Galway, Ireland 2 Massachusetts General Hospital, Boston, USA 3 Harvard Medical School, Boston, USA 4 School of Engineering and Informatics, NUI Galway, Ireland
    • 2. Motivation
      • To provide a complete RDF-based model to model online activities and scientific argumentation in neuromedicine:
        • Combining Web 2.0 shared knowledge using SIOC and formal scientific data (hypotheses, claims, dialogue, evidence, publications, etc.) via SWAN
      • To make (both formal and informal) discourse concepts and relationships more accessible to computation:
        • So that they can be better navigated, compared and understood both across and within domains
    • 3. How is this achieved?
      • An alignment of ontologies was performed to provide a complete framework for modelling activities in scientific communities
      • SWAN objects were integrated into SIOC Types module
      • SWAN was reused to model argumentative discussions
      • External models such as SCOT and MOAT were reused for tagging
      • SCF is being updated so that it can create data according to this model
    • 4. Collaborative websites are like data silos * Source: Pidgin Technologies, www.pidgintech.com
    • 5. Many isolated communities of users and their data * Source: Pidgin Technologies, www.pidgintech.com
    • 6. Need ways to connect these islands * Source: Pidgin Technologies, www.pidgintech.com
    • 7. Allowing users to easily move from one to another * Source: Pidgin Technologies, www.pidgintech.com
    • 8. Enabling users to easily bring their data with them * Source: Pidgin Technologies, www.pidgintech.com
    • 9. Types of data silos (scientific and social)
      • Collaborative websites used by scientific researchers in various domains:
        • SWAN/SCF is being used to connect these
      • Social websites used by people collaborating or communicating through the Web 2.0 platform:
        • SIOC is being used to connect these
      • SWAN/SIOC connects both sets of data silos together, not just structures but what is embedded within content as well
    • 10. SWAN (Semantic Web Applications in Neuromedicine)
      • An ontology of scientific discourse (Ciccarese et al. 2008)
      • A participatory knowledge base of hypotheses, claims, evidence and concepts in biomedicine, with the first instance in the domain of Alzheimer’s disease (AD)
      • Currently being integrated with the SCF (Science Collaboration Framework) toolkit for biomedical web communities
      • http://swan.mindinformatics.org/
    • 11. What does SWAN consist of?
      • A formal structure to record and present scientific discourse
      • Tools for scientists to manage, access and share knowledge
      • Tools for discovering conflicts, gaps and missing evidence
      • An information bridge to promote collaboration
      • A community process built upon the Alzforum site
    • 12. Main concepts and relationships in the SWAN ontology
    • 13. Modules in the SWAN ontology
    • 14. A typical hypothesis
    • 15. Contributions from leading researchers Key research topics Contribute content Inventory of ideas Mechanisms of disease
    • 16. Scientist view Toxic protein fragments believed responsible for AD Key information, gaps and conflicts Computer view Knowledge organised for computer processing, integration and reasoning
    • 17. Browsing evidence and inconsistencies
      • New experiment required?
    • 18. A researcher-supported effort
      • Dozens of etiopathological AD models annotated by SWAN curators in collaboration with leading researchers
      • Content reviewed before release by over twenty senior AD researchers
      • Software features reviewed before release by over thirty senior AD researchers
      • Extensive feedback incorporated into SWAN, such that this is a community tool (in line with Web 2.0 principles)
    • 19. Semantically-Interlinked Online Communities (SIOC)
      • An effort from DERI, NUI Galway to discover how we can create / establish ontologies on the Semantic Web
      • Goal of the SIOC ontology is to address interoperability issues on the (Social) Web
      • http://sioc-project.org/
      • SIOC has been adopted in a framework of 50 applications or modules deployed on over 400 sites
      • Various domains: Web 2.0, enterprise information integration, HCLS, e-government
    • 20.  
    • 21. The steps taken
      • Develop an ontology of terms for representing rich data from the Social Web
      • Create a food chain for producing, collecting and consuming SIOC data
      • As well dissemination via papers about SIOC, provide docs and examples at sioc-project.org
      • SIOC aims to enrich the Web infrastructure:
        • During the next upgrade cycle, gigabytes of semantically-enriched community data become available!
    • 22. Some of the SIOC core ontology classes and properties
    • 23. Some examples of where SIOC is already use (about 50 applications / modules)
    • 24. Creating a Social Semantic Web of previously-disconnected social “data silos”
    • 25. Also integrating scientific “data silos” in a semantic scientific collaboration framework
      • Enabling researchers to:
        • Collect data
        • Draw conclusions
        • Gather information
        • Create/modify hypotheses
        • Perform experiments
      • But with the benefit of cross-community and cross-domain experiences and results
    • 26. Mappings between SWAN and SIOC at http://rdfs.org/sioc/swan in OWL-DL
    • 27. Mappings between SWAN and SIOC classes
      • Subclasses of sioc:Item:
        • swanscidis:DiscourseElement
        • swanscidis:ResearchStatement
        • swanscidis:ResearchQuestion
        • swanscidis:ResearchComment
        • swancit:Citation
        • swancit:JournalArticle
      • Other mappings:
        • sioc:Post > swancit:WebArticle, swancit:WebNews
        • sioc:Comment > swancit:WebComment
      • swanscidis is the Scientific Discourse module, which provides a set of classes and properties to represent discourse elements
      • swancit is the Citations module, which aims to model the various citation elements that occur in scientific publishing
    • 28. Mappings between SWAN and SIOC properties
      • Subtypes of sioc:related_to:
        • swandisrel:agreesWith / swandisrel:disagreesWith
        • swandisrel:alternativeTo
        • swandisrel:arisesFrom
        • swandisrel:cites
        • swandisrel:consistentWith / swandisrel:inconsistentWith
        • swandisrel:discusses
        • swandisrel:inResponseTo
        • swandisrel:motivatedBy
        • swandisrel:refersTo
      • swandisrel is the Scientific Discourse Relationships module, which collects some of the relationships used for modelling discourse
      • May also use sioc:Item dcterms:hasPart swanscidis:DiscourseElement, for example, to represent that a particular hypothesis is part of a blog post
    • 29. Mappings redundancy
      • Redundant mappings:
        • Can be entailed thanks to the transitivity of rdfs:subClassOf / rdfs:subPropertyOf
        • e.g. “swancit:JournalArticle rdfs:subClassOf sioc:item” can be inferred from “swancit:JournalArticle rdfs:subClassOf swancit:Citation” and “swancit:Citation rdfs:subClassOf sioc:Item”
      • However:
        • SIOC applications generally do not support such chained entailments
        • Need to address lightweight inference
        • Therefore we provide direct rdfs:subClassOf mappings
    • 30. Querying mappings
      • Simple query to identify relatedness between items:
        • Applying a SIOC query over SWAN data
        • SPARQL / Pellet, files loaded on runtime in memory
        • Experiment with both simple mappings (including transitive closure) and full mappings
      PREFIX sioc: <http://rdfs.org/sioc/ns#> SELECT DISTINCT ?s ?o WHERE { ?s sioc:related_to ?o . ?s a sioc:Item . ?o a sioc:Item . }
    • 31. W3C HCLS Interest Group notes published
      • http://www.w3.org/TR/hcls-sioc/
      • http://www.w3.org/TR/hcls-swan/
      • http://www.w3.org/TR/hcls-swansioc/
    • 32. RDFa support in Drupal 7 for SSW data
    • 33. Exposing scientific results to search
      • Yahoo! Search Monkey and Google Rich Snippets
      • Highlights the structured data embedded in web pages
      • Google developers have indicated that scholarly publications marked up with Rich Snippets will also be picked up and appropriately indexed by Google Scholar
    • 34. Acknowledgements
      • We would like to thank Science Foundation Ireland for their support under grant SFI/08/CE/I1380 (Líon 2)
      • We would also like to thank an anonymous foundation for a generous gift in support of this work
      • Thanks to members of the W3C HCLSIG, in particular:
        • Susie Stephens
        • Scott Marshall
        • Eric Prud’hommeaux
    • 35. Motivation
      • To provide a complete RDF-based model to model online activities and scientific argumentation in neuromedicine:
        • Combining Web 2.0 shared knowledge using SIOC and formal scientific data (hypotheses, claims, dialogue, evidence, publications, etc.) via SWAN
      • To make (both formal and informal) discourse concepts and relationships more accessible to computation:
        • So that they can be better navigated, compared and understood both across and within domains

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