SWAN/SIOC: Aligning Scientific Discourse Representation and Social Semantics

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 School of Engineering and Informatics

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 School of Engineering and Informatics

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 School of Engineering and Informatics

4. Collaborative websites are like data silos School of Engineering and Informatics * Source: Pidgin Technologies, www.pidgintech.com

5. Many isolated communities of users and their data School of Engineering and Informatics * Source: Pidgin Technologies, www.pidgintech.com

6. Need ways to connect these islands School of Engineering and Informatics * Source: Pidgin Technologies, www.pidgintech.com

7. Allowing users to easily move from one to another School of Engineering and Informatics * Source: Pidgin Technologies, www.pidgintech.com

8. Enabling users to easily bring their data with them School of Engineering and Informatics * 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 School of Engineering and Informatics

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/ School of Engineering and Informatics

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 School of Engineering and Informatics

12. Main concepts and relationships in the SWAN ontology School of Engineering and Informatics

13. Modules in the SWAN ontology School of Engineering and Informatics

14. A typical hypothesis School of Engineering and Informatics

15. Contributions from leading researchers Inventory of ideas Mechanisms of disease Key research topics Contribute content School of Engineering and Informatics

16. Computer view Knowledge organised for computer processing, integration and reasoning Scientist view Toxic protein fragments believed responsible for AD Key information, gaps and conflicts School of Engineering and Informatics

17. Browsing evidence and inconsistencies New experiment required? School of Engineering and Informatics

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) School of Engineering and Informatics

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 School of Engineering and Informatics

20. School of Engineering and Informatics

21. The steps taken • Develop an ontologyof terms for representing rich data from the Social Web • Create a food chainfor producing, collecting and consuming SIOC data • As well disseminationvia 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! School of Engineering and Informatics

22. Some of the SIOC core ontology classes and properties School of Engineering and Informatics

23. Some examples of where SIOC is already use (about 50 applications / modules) School of Engineering and Informatics

24. Creating a Social Semantic Web of previously-disconnected social “data silos” School of Engineering and Informatics

25. 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 Also integrating scientific “data silos” in a semantic scientific collaboration framework School of Engineering and Informatics

26. Mappings between SWAN and SIOC at http://rdfs.org/sioc/swan in OWL-DL School of Engineering and Informatics

27. 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 Mappings between SWAN and SIOC classes School of Engineering and Informatics

28. 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 Mappings between SWAN and SIOC properties School of Engineering and Informatics

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 School of Engineering and Informatics

30. Querying 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 . } • 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 School of Engineering and Informatics

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/ School of Engineering and Informatics

32. RDFa support in Drupal 7 for SSW data School of Engineering and Informatics

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 School of Engineering and Informatics

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 School of Engineering and Informatics

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