Call for Papers: Special Issue on "Visual Exploration and Analysis of Linked Data"

Call for papers: Special Issue on

Visual Exploration and Analysis of Linked Data

Linked Data continues on its exponential growth path, to include manually curated datasets for specific tasks and domains and heterogeneous data generated in the online and physical worlds, as the modern, technology-rich and dependent user carries out ordinary activities in today's increasingly inter-connected, knowledge-driven world. Analysis and effective use of Linked Data however still lags far behind its generation rate. The content of this vast, rich knowledge resource therefore remains to a large extent untapped. Challenges faced in browsing, analysing, consuming and reusing Linked Data exist, within the Semantic Web community, the wider research community, industry and the lay public.

Within the research community the potential of Linked Data and the acknowledged challenges in its use are reflected in conference sub-themes, workshops and challenges. This is in addition to industry and research council-funded projects addressing varying aspects concerning the use of Linked (Open) Data. In industry, these and related challenges are increasingly being tackled under the umbrella of Big Data and Big Data Analytics. For the ordinary end user, Linked Data may be seen either as a portal to effective, timely, context-based knowledge use, or a technological barrier to effective use of the touted knowledge source.

In all three cases, the need to provide usable means for harnessing the knowledge content of Linked Data is a given. This special issue aims to address this topic in what is an important, albeit still a nascent area of research – visualisation of Linked Data. Visualisation provides an intuitive, yet powerful means to analyse complex data and situations, taking advantage of human perceptual abilities to, e.g., detect patterns within data or quickly identify outliers. Appropriately designed visualisation is able to support effective, exploratory knowledge discovery, trigger new insight, and therefore enable its users to obtain both an overview of the data and recognise and delve into detail in regions of interest. Visualisation therefore supports more effective analysis, and increased confidence in drawing conclusions about the underlying data and in decision-making.

While visualisation in this field very often is geared toward applications and reuse of Linked Data, as research progresses, new algorithms and custom approaches to visualisation are being developed specifically for Linked Data. This is very often under the umbrella of the Semantic Web, but increasingly also falls within the Information Visualisation and Visual Analytics communities. We invite submissions that demonstrate novel and adapted techniques, and innovative approaches to visual presentation, exploration and analysis of Linked Data, targeted at Semantic Web experts, (data) domain experts and/or the lay user.

We solicit submissions that:

  1. describe novel tools designed to handle the challenges specific to visual exploration and analysis of Linked Data.
  2. demonstrate the use of visualisation and visual analytics to:
    • derive insight and extract knowledge from Linked Data,
    • support the generation and curation of new Linked Datasets, or extend selected topics within the current Linked Data Cloud.
  3. illustrate the use of Linked Data to support or drive visual analytics and visualisation-based decision-making.
  4. describe use cases in which visualisation of Linked Data is employed toward reaching a specified end goal, or is applied with respect to a specified user type, task(s) and/or domain.
  5. evaluate visual approaches for browsing, analysing and consuming Linked Data.

Ontologies, particularly, are a vital component in the generation and use of Linked Data, allowing community-specific terms and colloquial use of language to be encoded using commonly agreed, formal terms or concepts, and providing a structured approach for capturing and encoding data with its context of use. Ontologies therefore allow more seamless linking of the independently generated datasets that form the Linked Data cloud. Effectiveness and correctness in the use of ontologies here impacts the use of related data subsets within the Linked Data cloud. We also invite submissions that illustrate how visualisation may be employed to augment the use of ontologies in the generation and consumption of Linked Data, and for linking data with related content but incompatible structure or representation.

Topics of Interest

Submissions may fall within, but are not limited to, the following topics and areas:

  1. Visualising, communicating and reusing the content of Linked Data, resolving where necessary, uncertainty, validity and dynamicity, via approaches including:
    • Visual analytics, considering both smaller sub-sets and domains within the Linked Data Cloud and scaling up to tasks requiring the traversal of multiple sub-sets, with inherent increase in data amount and complexity.
    • Collective intelligence
    • Search & discovery
    • Knowledge and content management
    • Ontology visualisation, analysis and usage
    • Social media data & networking
  2. Application domains, using both open and restricted access linked data
    • Business, marketing & logistics
    • the Media
    • Government, public bodies & services, e.g., education, health, social welfare & housing
    • Scientific Disciplines that make use of Linked Data and principles and techniques for analysing Linked Data, such as Bioinformatics
    • Travel, Culture, Entertainment & Events
    • Geo-location
  3. Evaluation & user studies
    • of visualisation-based approaches for browsing and analysing Linked Data
    • Visualisation as a means to improve usability of Linked Data
    • Usability studies & comparative analysis of existing visualisation approaches and tools
    • Benchmarks & metrics to support the evaluation of visualisation approaches and tools
  4. Inter-disciplinary approaches to the application of Linked Data, such as Social and Web science studies.
  5. Case studies examining application within research, in industry and for the lay user.

Submissions

December 15, 2014 - Paper submission deadline (extended!)

Papers may be submitted any time up till the closing date. Submissions will be assigned for review on receipt.

Submissions shall be made through the Semantic Web journal website at http://www.semantic-web-journal.net. Prospective authors must take notice of the submission guidelines posted at http://www.semantic-web-journal.net/authors. Note that you need to request an account on the website for submitting a paper. Please indicate in the cover letter that it is for the Visual Exploration and Analysis of Linked Data special issue.

Submissions are possible in all standing paper type of the journal, see http://www.semantic-web-journal.net/authors for descriptions: full research papers, surveys, linked dataset descriptions, ontology descriptions, application reports, tool/systems reports. While there is no upper limit, paper length must be justified by content.

Guest editors

Aba-Sah Dadzie, The HCI Centre, Sch. of Computer Science, The University of Birmingham, UK

Emmanuel Pietriga, INRIA (France) & INRIA Chile

The editors can be reached by email to swj2014.linked.data@gmail.com

Guest Editorial Board

to be completed

Steffen Lohmann, Institute for Visualization and Interactive Systems, University of Stuttgart, Germany
Heiko Paulheim, Research Group Data and Web Science, University of Mannheim, Germany
Mariano Rico, Ontology Engineering Group, Artificial Intelligence Dept., Universidad Politecnica de Madrid, Spain
John Howse, University of Brighton, UK
Luc Girardin, Macrofocus GmbH, Switzerland
Roberto Garci­a Gonzalez, Universitat de Lleida, Spain
Bernhard Schandl, mySugr GmbH, Vienna, Austria
Willem van Hage, SynerScope
Tomi Kauppinen, Cognitive Systems, University of Bremen, Germany
Mark Gahegan, Centre for eResearch, University of Auckland, New Zealand
Thomas Wischgoll, Wright State University, USA
Jan Polowinski, TU Dresden, Germany

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