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