1. SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics
Alexandre Passant1, Paolo Ciccarese2, 3, John G. Breslin4, Tim Clark2, 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
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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
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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
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4. Collaborative websites are like data silos
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* Source: Pidgin Technologies,

5. Many isolated communities of users and their data
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* Source: Pidgin Technologies,

6. Need ways to connect these islands
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* Source: Pidgin Technologies,

7. Allowing users to easily move from one to another
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* Source: Pidgin Technologies,

8. Enabling users to easily bring their data with them
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* Source: Pidgin Technologies,

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
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10. SWAN (Semantic Web Applications in Neuromedicine)
An ontology of scientific discourse (Ciccarese et al )
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
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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
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12. Main concepts and relationships in the SWAN ontology
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13. Modules in the SWAN ontology
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14. A typical hypothesis
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15. Contributions from leading researchers
Key research topics
Mechanisms of disease
Inventory of ideas
Contribute content
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16. Computer view Scientist view
Knowledge organised for computer processing, integration and reasoning
Scientist view
Toxic protein fragments believed responsible for AD
Key information, gaps and conflicts
[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>.
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17. Browsing evidence and inconsistencies
New experiment required?
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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)
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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
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
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20. School of Engineering and Informatics

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!
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22. Some of the SIOC core ontology classes and properties
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23. Some examples of where SIOC is already use (about 50 applications / modules)
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24. Creating a Social Semantic Web of previously-disconnected social “data silos”
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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
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26. Mappings between SWAN and SIOC at http://rdfs.org/sioc/swan in OWL-DL
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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
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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
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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
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30. Querying mappings Simple query to identify relatedness between items:
PREFIX sioc: <
SELECT DISTINCT ?s ?o
WHERE {
?s sioc:related_to ?o .
?s a sioc:Item . ?o a sioc:Item . }
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
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31. W3C HCLS Interest Group notes published
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32. RDFa support in Drupal 7 for SSW data
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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
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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
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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
School of Engineering and Informatics