SWCS 2014 - 3rd Workshop on Semantic Web Collaborative Spaces (SWCS2014)

Collaboration between data producers and consumers is a key challenge for facilitating the evolution of the LOD cloud into a participative and updatable LOD cloud. Semantic Web Collaborative Spaces aim to make open data producers and consumers working together to enhance and maintain linked data and contents, and improve linked data quality. These collaborative spaces include social semantic frameworks such as crowdsourcing tools, semantic wikis, semantic social networks, semantic microblogs; they have the mission to bring together human and software agents in order to foster knowledge ?intensive collaboration, content creation and management, annotated multimedia collection management, social knowledge diffusion and formalizing, and more generally speaking ontology? oriented content management life?cycle.
Goal and Motivations
Even after the early success of Linked Open Data initiatives, there remain significant bottlenecks and technical limitations that prevent the Linked Open Data (LOD) cloud from realizing its maximum potential - many that could be alleviated through the use of collaborative spaces. First, the LOD cloud is comprised of a large number of datasets published by autonomous data providers. Linked data is essentially read-only and most collaborative tasks of cleaning, enriching and reasoning are not dynamically available, i.e., there is no way to merge data or detect, on-the-fly, if faulty resources are going to be integrated with data in the LOD cloud. Second, open data are fragmented in multiple datasets, which have high level of semantic heterogeneity, i.e., many open (dynamic) data are currently not available in linked data formats. Thus, transferring continuously data to the LOD cloud is a complex and costly process; additionally, linked data can be outdated. Finally, devices that produce data dynamically respond to different technologies and may not respect Web-based protocols, e.g., sensors commonly utilize a wide variety of communication protocols. All these drawbacks inhibit data producers and consumers to work together to better manage resources. Unless these limitations are addressed, the LOD cloud will experience the following threats and limitations:
Emergence of a fork of open, time-dependent data and federation of linked data infrastructures, dividing resources and communities.
Low quality and limited availability of data may result in dissatisfied data consumers, and consequently unsupported investments of data producers.
Collaboration between data producers and consumers is a key challenge for overcoming the previous drawbacks, and facilitating the evolution of the LOD cloud into a participative and updatable LOD cloud. Semantic Web Collaborative Spaces aim to make open data producers and consumers working together to enhance and maintain linked data and contents, and improve linked data quality. These collaborative spaces include social semantic frameworks such as crowdsourcing tools, semantic wikis, semantic social networks, semantic microblogs; they have the mission to bring together human and software agents in order to foster knowledge-intensive collaboration, content creation and management, annotated multimedia collection management, social knowledge diffusion and formalizing, and more generally speaking ontology-oriented content management life-cycle.
Topics:
Contributions to this workshop will address one or more of the following topics:
Collaborative data sharing with SWCS:
Change management, truth maintenance, versioning, and undoing semantic changes.
Producing and Consuming Writable Linked Open Data.
Analyzing and Mining Writable Linked Open Data.
SWCS frameworks to enhance Linked Data Quality.
Transactional updates on Linked Open Data.
Representing and reasoning on semantics in social web platforms:
Reconciling formal semantics and social semantics.
Semantic social network analysis, community detection and community building.
Analyses of semantic wiki contributors and their contributions.
Combining, transforming, translating formal and informal knowledge.
Coping with disagreement, inconsistencies.
Semantics in social/human computing, and vice versa.
Connecting knowledge and social interaction from asynchronous interactions to real-time/multi-synchronous interactions in SWCS.
Optimizing, distributing, and scaling SWCS.
Managing and exploiting the emergence of models and their semantics.
Interacting with and within SWCS:
Browsing, navigating, visualizing.
Editing Linked Open Data, schemas, rules, etc.
Ergonomics of SWCS, interaction design and usability studies.
Object-centered sociality, knowledge-centered sociality.
Overcoming entrance barriers and giving incentives for contributing.
Provenance, traceability, permissions, trust, licensing, access control, privacy.
Making formal knowledge accessible, social knowledge evaluation.
Mobile and multimodal accesses to SWCS.
Return on experience and applications of semantic web collaborative spaces:
SWCS platforms in e-science, e-learning, e-health, e-governement, and life sciences.
Enterprise workflows, document flows, business intelligence, technological watch.
Corporate knowledge management or personal information management.
Expert matching, team creation.
Integration, interoperability and reuse of web collaborative spaces:
Integration and interoperability with other semantic applications and mashups.
Interlinking, distributing, and federating SWCS.
Extending non-semantic social web platforms with semantics.
Exporting and reusing semantics gained from SWCS.
Steering Committee
Pascal Molli, LINA, Nantes University (FR) (chair)
John Breslin, DERI NUI Galway (IE)
Maria-Esther Vidal, University Simon Bolivar (VE)