=Paper=
{{Paper
|id=Vol-1224/paper10
|storemode=property
|title=LinDA - Visualising and Exploring Linked Data
|pdfUrl=https://ceur-ws.org/Vol-1224/paper10.pdf
|volume=Vol-1224
|dblpUrl=https://dblp.org/rec/conf/i-semantics/ThellmannOA14
}}
==LinDA - Visualising and Exploring Linked Data==
https://ceur-ws.org/Vol-1224/paper10.pdf
LinDA - Visualising and Exploring Linked Data
Klaudia Thellmann, Fabrizio Orlandi, Sören Auer
University of Bonn & Fraunhofer IAIS, Bonn, Germany
klaudia.thellmann@iais.fraunhofer.de, orlandi@iai.uni-bonn.de,
auer@cs.uni-bonn.de
Abstract. The main goal of our work in the context of the LinDA
(Linked Data Analytics) project is to offer small and medium sized enter-
prises (SMEs) possibilities for integrating and consuming data by using
Linked Data technologies. One of the major challenges of this project con-
sists in providing user-friendly means of exploring and visualising Linked
Data. To achieve this, a Semantic Web application has been created,
based on state-of-the-art linked data visualisation approaches, which al-
lows a largely automatic matching and binding of data to visualisations.
Hence, in this demo paper we demonstrate the potential of a visualisa-
tion framework which is capable of dealing with different data formats,
serialisations and Semantic Web ontologies.
Keywords: linked open data, open data consumption, visualisation
1 Introduction
The increasing number of publicly available datasets poses a challenge regard-
ing the integration and consumption of information. The aim of the LinDA
project1 is to make the benefits of Linked Open Data accessible to SMEs and
data providers by providing libraries for Open Data consumption. One of the
main tasks in this context is to build an ecosystem of tools for visualising Linked
Data to assist SMEs in their daily tasks by hiding complexity through automa-
tion and an intuitive user interface. To complete this task, a generic visualisation
workflow2 is being implemented based on state-of-the-art Linked Data visuali-
sation approaches [1][3]. Most existing approaches are only usable by a technical
audience or limited to certain domains or data representations [2]. Voigt et al.
propose a generic approach for visualisation selection in form of a faceted browser
that imposes on the user the task of describing the visualisation at an unfamiliar
level of abstraction.
By taking the well-established visualisation tool Tableau Public3 as an upper
boundary regarding complexity we aim to find a balance between generality and
ease of use. Hence, we aim at improving on existing approaches and supporting
the user in visualising arbitrary Linked Data through automatic visualisation
1
http://linda-project.eu/
2
A demonstration of the prototype is available at http://goo.gl/bSgvjn
3
http://www.tableausoftware.com/public/
40 Klaudia Thellmann, Fabrizio Orlandi, Sören Auer
recommendation and pre-configuration and intuitive customization. For this we
consider using the visualisation ontology proposed by Voigt et al. besides the
other components for conversion, data analysis and (pre-)selection developed
within the LinDA project in order to realize and evaluate a generic and user-
centered visualisation workflow.
2 LinDA Visualisation Workflow
The visualisation workflow, as depicted in Figure 1, is used for supporting the
user in selecting and configuring visualisations and consists of the following steps:
1. Select data: The user starts with the selection of the dataset she intends to
visualise (Fig. 2, left). The input data needs to be either in RDF or tabular
format in order to proceed to the next step.
2. Select visualisation: Based on the content and format of the data and
the semantic descriptions of the available visualisation widgets, a ranking of
possible visualisations is computed and presented to the user (Fig. 2, right).
3. Configure visualisation: After choosing a visualisation from the list of
recommended visualisations, the user proceeds to the configuration step.
Here, she needs to provide the input necessary for the application in order to
map the data to the chosen visualisation (Fig. 3, top). Data conversion may
be performed automatically at this stage in case the selected visualisation
widget requires a different format.
4. Visualise: Finally, following the configuration step, the visualisation and
exploration phase of the input data can be performed (Fig. 3, bottom). At
this stage, the user can further customise the visualisation or export it in
different formats, share or publish it or save it for later reuse.
Fig. 1. The Linked Data Visualisation Workflow.
Posters & Demos Track @ SEMANTiCS2014 41
Fig. 2. Datasource and visualisation widget selection
In the following sections, we briefly introduce the approaches for the automatic
suggestion of suitable visualisations and the automatic mapping of input data
to a visualisation being developed.
Recommending Visualisations In order to rank suitable visualisations for
RDF input data, a similarity measure between the vocabularies used in the input
data and the vocabularies supported by the visualisation widgets is calculated.
For RDF input data the list of visualisations can also include visualisations with
different input formats if corresponding converters are present. If the data is in
a non-RDF format, the recommendation is based solely on the format, without
similarity calculation or other recommendation strategies.
Mapping Data to Visualisations The approach used for automatically map-
ping data to visualisations is an extension of the state-of-the-art LDVM [1]. Each
visualisation is described by a visualisation model which consists of (1) an in-
put data type, e.g. cube (table), network or tree, and (2) configuration options
defining structure and layout of the visualisation. For instance, a bar chart has
the cube data model and has the mapping of CSV columns or RDF properties to
vertical and horizontal axes as structure options and the axis labels as layout op-
tions. The approach for mapping data to visualisations consists of the following
steps: First, the appropriate input data type is determined based on the content
and format of the data. Then, a configuration form is composed based on the
configuration options of the visualisation, which can be pre-configured automat-
ically if the data is in RDF format (Fig. 3). The content of the configuration
form can vary depending on the input data type (e.g. for CSV data, the axes
of a bar chart can be mapped to the columns of the input file, while for RDF
data, the mapping is realised according to the properties of the instances of a
class selected by the user). A pre-configuration is created in both cases, but as
in CSV little semantic information is present, it can only be created on a low
level. In case of RDF, however, it can be automatically inferred from the RDF
42 Klaudia Thellmann, Fabrizio Orlandi, Sören Auer
Fig. 3. Configuration and visualisation
vocabulary of the input data by linking properties of a visualisation to properties
of a vocabulary.
3 Conclusion and Future Work
In this demo paper, we have introduced a generic approach for automatically
suggesting visualisations and binding data of different formats and vocabularies,
with the goal of providing SMEs with an intuitive way of exploring and visual-
ising data, especially Linked Data. In the future we plan to: (1) expanding the
range of input datatypes, e.g. networks, trees (2) introduce a generic ontology
for describing data and visualisation widgets (3) improve the user experience by
introducing intuitive configuration form templates for selecting and exploring
RDF data, and (4) conduct an extensive evaluation through a user study.
References
1. Josep Maria Brunetti, Sören Auer, Roberto Garcı́a, Jakub Klı́mek, and Martin
Nečaský. Formal linked data visualization model. In Proc. IIWAS, IIWAS ’13,
pages 309–318, NY, 2013. ACM.
2. Aba-Sah Dadzie and Matthew Rowe. Approaches to visualising linked data: A
survey. Semantic Web, 2(2):89–124, 2011.
3. Martin Voigt, Stefan Pietschmann, and Klaus Meißner. A semantics-based, end-
user-centered information visualization process for semantic web data. In Semantic
Models for Adaptive Interactive Systems, pages 83–107. Springer, 2013.