swan/sioc: aligning scientific discourse representation and social semantics
DESCRIPTION
Semantic Web Applications in Scientific Discourse Workshop at the International Semantic Web Conference / Washington, DC / 26th October 2009TRANSCRIPT
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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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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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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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Collaborative websites are like data silos
* Source: Pidgin Technologies, www.pidgintech.com
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Many isolated communities of users and their data
* Source: Pidgin Technologies, www.pidgintech.com
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Need ways to connect these islands
* Source: Pidgin Technologies, www.pidgintech.com
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Allowing users to easily move from one to another
* Source: Pidgin Technologies, www.pidgintech.com
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Enabling users to easily bring their data with them
* Source: Pidgin Technologies, www.pidgintech.com
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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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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/
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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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Main concepts and relationships in the SWAN ontology
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Modules in the SWAN ontology
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A typical hypothesis
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Contributions from leading researchers
Key research topics
Contribute content
Inventory of ideas
Mechanisms of disease
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Scientist viewToxic protein fragments believed responsible for AD
Key information, gaps and conflicts
Computer viewKnowledge organised for
computer processing, integration and reasoning
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Browsing evidence and inconsistencies
New experiment required?
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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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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
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The steps taken
1. Develop an ontology of terms for representing rich data from the Social Web
2. Create a food chain for producing, collecting and consuming SIOC data
3. 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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Some of the SIOC core ontology classes and properties
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Some examples of where SIOC is already use (about 50 applications / modules)
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Creating a Social Semantic Web of previously-disconnected social “data silos”
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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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Mappings between SWAN and SIOC at http://rdfs.org/sioc/swan in OWL-DL
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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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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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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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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 ?oWHERE {?s sioc:related_to ?o .?s a sioc:Item . ?o a sioc:Item .}
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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/
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RDFa support in Drupal 7 for SSW data
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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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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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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