=Paper=
{{Paper
|id=Vol-2578/BigVis9
|storemode=property
|title=Providing Effective Visualizations over Big Linked Data
|pdfUrl=https://ceur-ws.org/Vol-2578/BigVis9.pdf
|volume=Vol-2578
|authors=Federico Desimoni,Laura Po
|dblpUrl=https://dblp.org/rec/conf/edbt/DesimoniP20
}}
==Providing Effective Visualizations over Big Linked Data==
https://ceur-ws.org/Vol-2578/BigVis9.pdf
Providing Effective Visualizations over Big Linked Data
Short Research Paper
Federico Desimoni Laura Po
federico.desimoni@unimore.it laura.po@unimore.it
“Enzo Ferrari" Engineering Department “Enzo Ferrari" Engineering Department
University of Modena and Reggio Emilia University of Modena and Reggio Emilia
Modena, Italy Modena, Italy
ABSTRACT LD by offering different levels of abstraction. The first version
The number and the size of Linked Data sources are constantly of H-BOLD appeared in 2018 [13, 15]. In this paper, we want to
increasing. In some lucky case, the data source is equipped with a describe the re-engineering process that has been carried out and
tool that guides and helps the user during the exploration of the the additional features developed.
data, but in most cases, the data are published as an RDF dump The architecture of the tool is introduced in Section 2, while
through a SPARQL endpoint that can be accessed only through the new features implemented are reported in Section 3. Related
SPARQL queries. Although the RDF format was designed to be work are described in Section 4. Section 5 sketches conclusion
processed by machines, there is a strong need for visualization and future work.
and exploration tools. Data visualizations make big and small
linked data easier for the human brain to understand, and visual- 2 H-BOLD
ization also makes it easier to detect patterns, trends, and outliers H-BOLD1 (High-level visualizations on Big Open Linked Data) is
in groups of data. a tool, available online, for visualizing, and interacting with LD.
For this reason, we developed a tool called H-BOLD (High- It is defined in the context of hierarchical and interactive visual
level Visualization over Big Linked Open Data). H-BOLD aims to exploration and analysis over LD [13].
help the user exploring the content of a Linked Data by providing H-BOLD starts from our past experience with the tool LODeX,
a high-level view of the structure of the dataset and an interactive a tool able to automatically provide a summarization of a LD,
exploration that allows users to focus on the connections and including its inferred schema [1–3, 5], and tried to overcome the
attributes of one or more classes. Moreover, it provides a visual main limitations arose during its evaluation [4]. The main goal
interface for querying the endpoint that automatically generates of H-BOLD was to facilitate the exploration of LD with a high
SPARQL queries. number of classes. The current architecture of H-BOLD is shown
in Figure 1 where the updates, with respect the previous version
KEYWORDS [13], are highlighted in blue.
Linked Data, Visualization, Big Data, SPARQL, Semantic Web, H-BOLD is composed of a server layer and a presentation layer
Visual Querying, Data Visualization that will be described in the following.
2.1 Server layer
1 INTRODUCTION
The server layer aims to generate high-level representations of a
Since 2006, the year in which sir Tim Berners-Lee coined the
set of LD starting from a list of SPARQL endpoints. The outputs
term Linked Data (LD), leading to a new way in which data can
are the Cluster Schema (a high level representation of a complex
be structured and accessed through the Internet, the number
and big source) and the Schema Summary (a low level repre-
of LD exploded. Starting from governments, many institutions,
sentation of the instanced classes within a source). The SPARQL
enterprises, and privates adopted this method for publishing data.
endpoint list is created starting from the old list of endpoints used
As a result, there is a huge number of LD that can be accessed
in LODeX and adding new SPARQL endpoints that are present
through the Internet. Unfortunately, endpoints are big containers
on Open data portals. Moreover, we enable users to manually
of triples. They can contain every kind of information and the
add a new URL for a SPARQL endpoint they wish to visualize
high number of triples required to express a concept made LD
and explore in H-BOLD. On each endpoint a set of queries is
visualization a non-trivial task. Due to the volume and the variety
executed in order to extract structural and statistical information
of information, it is hard to find a common procedure for explor-
that describe the LD, this phase is called Index Extraction. In
ing every dataset but nonetheless, several research groups tried
particular, the indexes are the number of instances, the number
to address this task [10, 12, 16, 17]. A hierarchical visualization
of classes, the list of classes with the respective properties and
can address the problem of information overloading, offering an
the number of instances belonging to a specific class. The Index
effective mechanism for information abstraction and summariza-
Extraction is able to deal with the performance issues of the dif-
tion. Additionally, an interactive exploration allows the user to
ferent implementations of SPARQL endpoints by using pattern
understand step-by-step the content of even complex and big LD.
strategies [1].
In this paper, we present a new version of the tool H-BOLD
Starting from the indexes is then possible to create a Schema
(High-level visualizations on Big Open Linked Data). H-BOLD
Summary of the LD, a pseudograph that represents, through
enables the exploratory search and multilevel analysis of Big
nodes and arches, the relations between the various instantiated
© 2020 Copyright for this paper by its author(s). Published in the Workshop Proceed- classes of the dataset [2, 5]. The Schema Summary is a good
ings of the EDBT/ICDT 2020 Joint Conference (March 30-April 2, 2020, Copenhagen, approach to compactly represent a RDF dataset, however when
Denmark) on CEUR-WS.org. Use permitted under Creative Commons License At-
tribution 4.0 International (CC BY 4.0)
1 https://dbgroup.ing.unimore.it/hbold/
Figure 1: Architecture and Workflow of H-BOLD
we are dealing with big sources, it happens that the number Another option for the user to start is from the exploration
of classes is high, thus the graph contains a high number of of the Schema Summary, here, he/she will see a complete graph
nodes and the visualization results complex and confused. On containing all the instantiated classes of the LD (see Figure 2,
the Schema Summary, a set of community detection techniques step 4). The user can focus on a particular class and explore its
has been used to create a high-level visualization for Big LD attributes and properties. Figure 2 shows the visualization steps
[15]. The classes, of the Schema Summary, are grouped into in H-BOLD starting from the Cluster Schema and then selecting
Clusters, therefore, a Cluster Schema is generated for each LD a class and expanding the graph till the visualization of the entire
where nodes are groups of classes and arches are connections Schema Summary over the Scholarly LD2 .
among these Clusters. In the clustering of the Schema Summary,
the possibility that a node belongs to several Clusters is avoided. 3 RE-ENGINEERING THE TOOL
The labels in the Cluster Schema are assigned based on the degree The H-BOLD application has been renovated both in the server
(the sum of in-degree and out-degree) of the classes (nodes) that layer and in the presentation layer. The server layer is imple-
are represented by the cluster. For the formal definition of the mented entirely in python and it uses MongoDB for the storage
Schema Summary and Cluster Schema see [15]. of information. With respect to the previous version of H-BOLD,
The Schema Summary and Cluster Schema offer several ad- it has been equipped with new features for making the applica-
vantages: they can be easily memorized and retrieved on the tion more responsive to the user requests. First of all, to enhance
MongoDB improving data recovery performance and graph visu- the understanding of the behavior of the tool, a guideline has
alization. been drawn up: the application code has been studied and vari-
ous flowcharts have been extracted. A process for continuously
2.2 Presentation layer updating and enriching our collection of SPARQL endpoints has
been implemented (see Subsection 3.1 for further details). The
In the presentation layer, the first step for a user is the selection Cluster Schema creation has been re-implemented and included
of a dataset, then, the user can start the exploration of the Cluster in the Server Layer, while previously was calculated on-the-fly
Schema or the exploration of the Schema Summary. The first is in the Presentation Layer (Subsection 3.2). The list of SPARQL
more concise, while the second is the complete visualization of endpoints has been enriched thanks to the integration of three
the structural information on the LD. new open data portals (Subsection 3.3).
If the user opts for the Cluster Schema (see Figure 2, steps 1), The presentation layer went through a complete re-engineer-
he/she will see a shrank representation of the Schema Summary ization. Due to many incompatibilities between the current brows-
that is obtained by applying a community detection algorithm. ers and the version of Polymer3 in use (i.e. a javascript framework
From the Cluster Schema, by selecting a class within a cluster, developed by Google that helps building web application through
a new visualization focused on the selected class is proposed the concept of web components), we were forced to re-implement
to the user. The user might then further explore the class, its the presentation layer of H-BOLD.
connections with other classes and its attributes, or can itera- We started searching alternatives that could replace Polymer.
tively increase the graph displayed by expanding the connections At the end of this research, we decided to move towards Boot-
starting from some classes (nodes in the graph). In each partial strap4 , a javascript framework developed by Twitter for build-
representation of the Schema Summary, the user is informed ing and styling web applications. Therefore, the interface of the
about the percentage of the instances represented by the graph web application has been completed rebuilt with Bootstrap. The
and the total number of nodes (see Figure 2, steps 2 and 3). This
2 http://www.scholarlydata.org/
expansion can be repeated until all the classes are displayed, as 3 https://www.polymer-project.org/
in the Schema Summary visualization. 4 https://getbootstrap.com/
Figure 2: Step-by-step visualization of the Scholary LD. From left to right: 1) visualization of the Cluster Schema, 2) explo-
ration of the "Event" class, 3) further expansion and exploration of the dataset, 4) complete visualization of the Schema
Summary.
javascript graphic library, D35 , already in use in H-BOLD, has 3.2 Cluster Schema visualization and
been integrated with Bootstrap to create interactive visualiza- generation
tions. Moreover, new features ameliorate H-BOLD. A new inter-
The Cluster Schema is the core part that has been inserted in
face for allowing user to manually insert a SPARQL endpoint
H-BOLD, starting from the predecessor LODeX. The Cluster
has been added (Subsection 3.4). New visualization layouts have
Schema allows to visualize in an aggregated way all the classes of
been conceived for the exploration of the Cluster Schema and
a LD. In the previous demo of H-BOLD [13], the Cluster Schema
the Schema Summary (Subsection 3.5).
was calculated on-the-fly by running the community detection
algorithm each time a user asked to see a Cluster Schema. This
3.1 Endpoint extraction automation and procedure shows different weak points. Since the Cluster Schema
updates is computed over the Schema Summary, if the Schema Summary
does not change then the Cluster Schema will not change neither,
Adding new content inside a SPARQL endpoint is a simple task
so it does not make sense to recompute the Cluster Schema on
since the only constraint is that data must be represented in one
each user click. Moreover, even if the community detection algo-
of the RDF serialization formats. Adding new classes or relations
rithm took short time to run, the user had to wait the information
is just as simple as adding new instances. As a consequence, the
to be both transformed and loaded before its visualization. For
structure and also the content of a LD could change very often.
these reasons, we inserted the computation of the community
In H-BOLD, we want to display the most updated version of the
detection algorithm on the server side layer and the storage of
dataset we indexed. For this reason, we automated the procedure
the Cluster Schema in the MongoDB. Now, the Cluster Schema
of indexes extraction and Schema Summary and Cluster Schema
is computed only once, after the index extraction procedure and
generation to run daily. Working with SPARQL Endpoints since
the Schema Summary computation, and then stored in the DB.
2014, we noticed two important aspects. First, a SPARQL End-
Therefore, both the Schema Summary and Cluster Schema can
point might be often not available6 , but this does not mean that
be visualized by directly querying the DB. Experimental results
it is completely out of order, it might work again after 1 or 2 days.
showed that, on half of the SPARQL endpoints stored in H-BOLD,
Second, LD do not change daily, they usually changed weekly, or
the time needed to display the Cluster Schema to the user is
monthly, or do not change ever. For these reasons, it is useless to
decreased by the 35%.
run the index extraction over all the datasets daily, it is enough to
run it weekly. However, since a SPARQL endpoint might not be
available one day, and maybe be online the next day, we should 3.3 Automatic insertion of SPARQL
check its availability. Therefore, we decided to store the date of endpoints by crawling open data portals
the last index extraction for each SPARQL endpoint. If the last In the previous version of H-BOLD, the list of datasets that users
index extraction was executed more than seven days before, then could visualize was manually created from a list of SPARQL
we do not update the information for that LD, however, if some endpoints available on DataHub. Unfortunately, the endpoint
things went wrong with the last index extraction because the we queried for obtaining such information is no more available
endpoint was not available, we can repeat the index extraction but the same data can be found at this link 7 . Moreover, that
every day. list was quite old and some of the endpoints were no longer
available. For this reason, we conducted a research to identify
which were the most relevant open data portals that contains
links to SPARQL endpoints. Then, we extended both the server
5 https://d3js.org/
6 https://sparqles.ai.wu.ac.at/availability 7 https://old.datahub.io/dataset/sparql-endpoint-status
Figure 3: Interface for the insertion of a SPARQL endpoint and the e-mail of the successfully extraction
layer and the presentation layer. The server layer was set up to 3.4 Manual insertion of new endpoints
query three new open data portals. In particular, now, H-BOLD Even with the crawling of SPARQL endpoint from open data
search for new endpoints in the following portals: portals, we are not able to reach, index and expose every SPARQL
• European Data Portal8 . It contains open data published endpoint available on internet. Therefore, to further increase
over the different european countries, regions and local the number of indexed datasets, we integrated in H-BOLD a
administrations open data portals; procedure through which the user is able to upload the URL of a
• EU Open Data Portal9 . It holds the data produced by the SPARQL endpoint and to see, this new dataset listed among the
different organization of the European Union; others in the H-BOLD dataset list (we tested this procedure over
• IO Data Science of Paris10 . Born with the idea of creat- some open datasets [6]). Since the index extraction procedure
ing new synergies between data, it contains a metadata can be time-consuming, the user is asked to provide an e-mail
description of several LD. address so that the system can notify he/she about the status of
the extraction. At the end of the extraction, the e-mail address
The portals were deeply explored in order to gain knowledge
is deleted, since we do not want to keep person data, while the
over their content. Initially, we tried to produce three ad-hoc
dataset is added to the list of available datasets. In this way,
queries for extracting the maximum amount possible of SPARQL
we enlarge the list of LD that H-BOLD is able to visualize and
endpoints but then we found that the query presented in Listing
we made H-BOLD more responsive to the user requests. Figure
1 perfectly fits all the portals.
3 contains the interface for uploading the URL of a SPARQL
endpoint and the email that will be sent to the users in case the
Listing 1: Query sent to the open data portals to extract a index extraction procedure is successful.
list of SPARQL endpoints
PREFIX d c a t : < h t t p : / / www. w3 . o r g / ns / d c a t # > 3.5 New visualizations
PREFIX dc : < h t t p : / / p u r l . o r g / dc / t e r m s / >
The previous version of H-BOLD adopted only graphs for display-
SELECT ? d a t a s e t ? t i t l e ? u r l ing the information (as shown in Figure 2) and in some occasion
WHERE { they were not suited for extracting the maximum of the infor-
? dataset a dcat : Dataset . mation. For instance, the number of instances belonging to a
? d a t a s e t dc : t i t l e ? t i t l e .
? dataset dcat : d i s t r i b u t i o n ? d i s t r i b u t i o n .
class is hard to extrapolate from a graph and it is also hard to
? d i s t r i b u t i o n dcat : accessURL ? u r l . understand which classes have been aggregate in what cluster.
f i l t e r ( regex ( ? u r l , ' s p a r q l ' ) ) . Moreover, when the number of classes and relations is high, it is
} hard to understand the various connection. For this reason we
With this research, we discovered 65 SPARQL endpoints on implemented four supplementary visualization layouts, three for
the European Data Portal, 9 SPARQL endpoints on the EU Open the Cluster Schema and one for the Schema Summary. We drew
Data Portal and 15 SPARQL endpoints on the IO Data Science of inspiration from an extensive analysis over other LD visualiza-
Paris. Some endpoints were already present in H-BOLD, therefore tion tools [14]. The new visualizations for the Cluster Schema
we were effectively able to increment the number of endpoints in allow displaying together the clusters and the classes, providing
our collection by 70 units. As a result, the number of endpoints users with a complete high-level overview of the dataset. The
listed in H-BOLD raised from 610 to 680. Since some of them new visualization for the Schema Summary allow to better under-
are not working or are not compatible with the index extraction standing the inter-connections among classes and the incoming
phase of H-BOLD, we were able to index and expose the structure and out-going properties.
of 20 new datasets raising the number of indexed endpoints from 3.5.1 Treemap visualizations of the Cluster Schema. Treemaps
110 to 130. are an alternative way of visualising the hierarchical structure of
a Cluster Schema while also displaying quantities for each cluster
8 https://www.europeandataportal.eu/ and each class within the cluster via area size. Each cluster is
9 https://data.europa.eu/euodp/en/home/ assigned to a rectangle area with a specific color and their classes
10 https://io.datascience-paris-saclay.fr
rectangles nested inside of it. When a quantity is assigned to
Figure 4: Treemap visualization of the Cluster Schema
a class, its rectangle area size is displayed in proportion to that
quantity and to the other quantities within the same cluster in
a part-to-whole relationship. Also, the area size of the cluster
is the total of its classes. If no quantity is assigned to a class,
then its area is divided equally amongst the other classes within
its cluster. The treemap built over the instance’s count, shown Figure 6: Circle Pack visualization of the Cluster Schema
in Figure 4), highlights the classes with the higher number of
instances, the size of the clusters and the predominance of some
represented as a circle and its sub-branches are represented as
classes in terms of instances.
circles inside it. Similarly to the rectangles in the Treemap, the
circles might have different dimensions. As displayed in Figure
6, the inner circles represent the classes, while the intermediate
circles represent the clusters, an external circle represents the
entire dataset. In some cases, a cluster can contain only one class.
Figure 5: Sunburst visualization of the Cluster Schema
3.5.2 Sunburst visualizations of the Cluster Schema. The Sun-
burst Chart visualization (Figure 5) shows the hierarchy through
a series of rings, that is sliced for each category node. The inner
ring represents the clusters while the outer ring shows the classes
grouped by the clusters. Figure 7: Hierarchical Edge graph visualization of the
Schema Summary
3.5.3 Circle Pack visualizations of the Cluster Schema. The
Circle Packing (Figure 6) is a variation of a Treemap that uses
circles instead of rectangles. Containment within each circle 3.5.4 Hierarchical edge bundling visualization for the Schema
represents a level in the hierarchy: each branch of the tree is Summary. Hierarchical edge bundling is a method developed by
Holten in 2006 [11] for allowing to visualize adjacency relations project funded by the “Enzo Ferrari" Engineering Department
between entities organized in a hierarchy. The idea is to bun- of the University of Modena and Reggio Emilia within FAR2019.
dle the adjacency edges together to decrease the clutter usually The contents of this publication are the sole responsibility of its
observed in complex networks. This data visualisation method authors and do not necessarily reflect the opinion of the European
allows to check connections between leaves (classes in our case) Union.
of a hierarchical network.
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