=Paper=
{{Paper
|id=None
|storemode=property
|title=The Linked Data Visualization Model
|pdfUrl=https://ceur-ws.org/Vol-914/paper_29.pdf
|volume=Vol-914
|dblpUrl=https://dblp.org/rec/conf/semweb/FerneandezAG12
}}
==The Linked Data Visualization Model==
https://ceur-ws.org/Vol-914/paper_29.pdf
The Linked Data Visualization Model
Josep Maria Brunetti1 , Sören Auer2 , Roberto García1
1
GRIHO, Universitat de Lleida, Jaume II, 69. 25001 Lleida, Spain
{josepmbrunetti,rgarcia}@diei.udl.cat, http://griho.udl.cat/
2
AKSW, Computer Science, University of Leipzig, Germany
auer@informatik.uni-leipzig.de, http://aksw.org/
Abstract. The potential of the semantic data available in the Web is
enormous but in most cases it is very difficult for users to explore and use
this data. Applying information visualization techniques to the Semantic
Web helps users to easily explore large amounts of data and interact with
them. We devise a formal Linked Data Visualization model (LDVM),
which allows to dynamically connect data with visualizations.
1 Introduction
In the last years, the amount of semantic data available on the Web has increased
dramatically, especially thanks to initiatives like Linked Open Data (LOD). The
potential of this vast amount of data is enormous but in most cases it is very dif-
ficult and cumbersome for users to visualize, explore and use this data, especially
for lay-users without experience with Semantic Web technologies.
Applying information visualization techniques to the Semantic Web helps
users to explore large amounts of data and interact with them. Visualizations
are useful for obtaining an overview of the datasets, their main types, properties
and the relationships between them. Compared to prior information visualiza-
tion strategies, we have a unique opportunity on the Data Web. The unified
RDF data model being prevalent on the Data Web enables us to bind data to
visualizations in an unforeseen and dynamic way. An information visualization
technique requires certain data structures to be present. When we can derive
and generate these data structures automatically from reused vocabularies or
semantic representations, we are able to realize a largely automatic visualization
workflow. This will enable users to explore datasets even if the publisher of the
data does not provide any exploration or visualization means.
The Linked Data Visualization Model (LDVM) we propose allows to con-
nect different datasets with different visualizations in a dynamic way. In order
to achieve such flexibility and a high degree of automation the LDVM is based
on a visualization workflow incorporating analytical extraction and visual ab-
straction steps. Each of the visualization workflow steps comprises a number of
transformation operators, which can be defined in a declarative way. As a result,
the LDVM balances between flexibility of visualization options and efficiency of
implementation or configuration.
2 Linked Data Visualization Model
We use the Data State Reference Model (DSRM) proposed by Chi [1] as con-
ceptual framework for our Linked Data Visualization Model (LDVM). While the
DSRM describes the visualization process in a generic way, we instantiate and
adopt this model with LDVM for the visualization of RDF and Linked Data.
The names of the stages, transformations and operators have been adapted to
the context of Linked Data and RDF. Figure 1 shows an overview of LDVM. It
can be seen as a pipeline, which originates in one end with raw data and results
in the other end with the visualization.
RDF DATA SPARQL
OPERATORS
DATA
TRANSFORMATION
ANALYTICAL EXTRACTION ANALYTICAL
OPERATORS
VISUALIZATION
TRANSFORMATION
VISUALIZATION ABSTRACTION VISUALIZATION
OPERATORS
VISUAL MAPPING
TRANSFORMATION
VIEW VIEW
OPERATORS
Fig. 1. High level overview of the Linked Data Visualization Model.
The LDVM pipeline is organized in four stages that data needs to pass
through:
1. RDF Data: the raw data, which can be all kinds of information adhering to
the RDF data model, e.g. instance data, taxonomies, vocabularies, ontolo-
gies, etc.
2. Analytical extraction: data extractions obtained from raw data, e.g. calcu-
lating aggregated values.
3. Visual abstraction: information that is visualizable on the screen using a
visualization technique.
4. View: the result of the process presented to the user, e.g. plot, treemap, map,
timeline, etc.
Data is propagated through the pipeline from one stage to another by apply-
ing three types of transformation operators:
1. Data transformation: transforms raw data values into analytical extractions
declaratively (using SPARQL query templates).
2. Visualization transformation: takes analytical extractions and transforms
them into a visualization abstraction. The goal of this transformation is to
condense the data into a displayable size and create a suitable data structure
for particular visualizations.
3. Visual mapping transformation: processes the visualization abstractions in
order to obtain a visual representation.
As illustrated in Figure 2, our model allows to connect different RDF datasets
and different data extractions with different visualization techniques. Not all
datasets are compatible with all data extractions and each data extraction is
only compatible with some visual configurations.
RDF Data Analytical Extraction Visualization Abstraction View
Datasets Data Configuration View
Dataset 1 Data 1 Config 1
View 1
Dataset 2 Data 2 Config 2
. . . View 2
.
. . . .
. . . .
Dataset n Data n Config n
View n
Data Transformation Visualization Transformation Visual Mapping Transformation
Compatible Incompatible
Fig. 2. Linked Data Visualization Model ecosystem, which allows to dynamically con-
nect datasets with visualizations.
Each dataset offers different data structures to be extracted, e.g. class hi-
erarchy, property hierarchy, geospatial data, etc. Each data extraction can be
visualized with different configurations, which contain information such as the
visualization technique to use, colors, etc. Then, a concrete visualization is gen-
erated depending on the data extraction and the visual configuration.
In summary, the model is divided in two main areas: data space and visual
space. The RDF data stage, analytical extraction stage and data transformation
belong to the data space, while visual abstraction stage, view stage and visual
mapping transformation belong to the visual space. These two main blocks are
connected by a visualization transformation.
3 Demonstration
We have implemented a prototype called LODVisualization 1 that supports the
Linked Data Visualization Model proposed. It allows to explore and interact
with the Data Web through different visualizations. This way, our prototype
serves not only as a proof-of-concept of our LDVM but also provides useful
visualizations of RDF. These visualizations allow users to obtain an overview of
RDF datasets and realize what the data is about: their main types, properties,
etc.
LODVisualization is compatible with most of SPARQL endpoints as long as
they support JSON and SPARQL 1.1. We have evaluated our implementation
of the Linked Data Visualization Model with different datasets, data extractions
and visualizations. The goal of our evaluation was to prove that the LDVM can
be applied to different datasets providing different data visualizations in real
time. All the visualization examples are available on the website and it easy to
create new ones.
4 Related Work
Some of the existing tools available to explore and visualize Linked Data have
been analyzed in [2]. However, only very few provide visualizations and they
are focused on concrete data types or domains.
5 Conclusions
The Linked Data Visualization Model (LDVM) can be applied to rapidly create
visualizations of RDF data. It allows to connect different datasets, different data
extractions and different visualizations in a dynamic way. Applying this model,
developers and designers can obtain a better understanding of the visualization
process with data stages, transformations and operators. The LDVM offers user
guidance on how to create visualizations for RDF data.
References
1. Ed H. Chi. A taxonomy of visualization techniques using the data state reference
model. In Proceedings of the IEEE Symposium on Information Vizualization 2000,
INFOVIS ’00, pages 69–, Washington, DC, USA, 2000. IEEE Computer Society.
2. Aba-Sah Dadzie and Matthew Rowe. Approaches to visualising linked data: A
survey. Semantic Web, 2(2):89–124, 2011.
1
http://lodvisualization.appspot.com/