=Paper=
{{Paper
|id=Vol-1472/IESD_2015_paper_2
|storemode=property
|title=Linked Data Maps: Providing a Visual Entry Point for the Exploration of Datasets
|pdfUrl=https://ceur-ws.org/Vol-1472/IESD_2015_paper_2.pdf
|volume=Vol-1472
|dblpUrl=https://dblp.org/rec/conf/semweb/ValsecchiABTM15
}}
==Linked Data Maps: Providing a Visual Entry Point for the Exploration of Datasets
==
https://ceur-ws.org/Vol-1472/IESD_2015_paper_2.pdf
Linked Data Maps: Providing a Visual
Entry Point for the Exploration of Datasets
Fabio Valsecchi, Matteo Abrate, Clara Bacciu,
Maurizio Tesconi, and Andrea Marchetti
Institute of Informatics and Telematics (IIT),
National Research Council (CNR), Pisa, Italy
{firstname.surname}@iit.cnr.it
http://wafi.iit.cnr.it
Abstract. Linked Data sets are an ever-growing, invaluable source of
information and knowledge. However, the wide adoption of this large
amount of interlinked structured data is still held back by some non-
trivial obstacles. The one we tackle in this article is the difficulty users
have in getting started with their work on Linked Data sources. In fact,
querying, and in general dealing with such datasets, requires a deep
knowledge about their specific classes, instances and properties. We be-
lieve that an entry point that eases the access to such information would
significantly reduce the barriers around this technology and foster its
promotion. Linked Data Maps is a method for representing RDF graphs
as interactive, map-like visualizations, based on our previous work fo-
cused on the visual exploration of DBpedia. The approach is extended
to deal with a wider range of Linked Data sets, and tested with a user
evaluation study on two distinct RDF graphs.
Keywords: Linked Data, Dataset Exploration, Information Visualiza-
tion
1 Introduction
The Linked Data project comprises a large, open and valuable collection of inter-
linked structured data over the World Wide Web. The potential of the numerous
data sources within the Linked Data Cloud1 is undeniable, and the enormous
amount of knowledge they contain is valuable for both public and private or-
ganisations. However, a wide adoption of Linked Data and Semantic Web tech-
nologies is still prevented by some relevant obstacles. One of these impediments
is that users are required a high effort in order to get started with the handling
of Linked Data sources. Approaching a Linked Data set remains, especially in
the first phase, a hard and onerous task even for expert users. In fact, accessing
and querying Linked Data sets require a deep knowledge about their classes, in-
stances and predicates. In general, users often need to figure out how a dataset
1
http://lod-cloud.net
2 F. Valsecchi, M. Abrate, C. Bacciu, M. Tesconi and A. Marchetti
is structured, how many instances it describes, how the ontology is organized,
what kind of instances it contains, how they are linked to each other, and so on.
Basically, the key question is “How is the dataset like?”. In our opinion, there is
a lack of effective and easy-to-master instruments for answering these important
questions. To the best of our knowledge, the currently available tools are mainly
focused on the presentation of aggregated data, or on the visualization of the
details related to a single instance or a small group of them, and do not show a
complete representation of the dataset.
In order to fill this gap, we think that applications acting as entry points to
Linked Data sets would ease the access to these large and complex data sources,
by guiding users from a high level overview to the most specific details of RDF
triples that compose them. We propose Linked Data Maps, an interactive vi-
sualization approach based on the work carried out on DBpedia in [25], which
is focused on the automatic transformation of an RDF graph into a map de-
signed according to cartographic principles. A map can leverage human visual
perception abilities and consolidated map-reading skills to achieve a high level of
efficacy in communicating the properties of large and complex structures such as
Linked Data sets [23, 1]. Moreover, an interactive zoomable map literally fits the
Visual Information-Seeking Mantra proposed by Ben Shneiderman (“Overview
first, zoom and filter, then details-on-demand”) [22, 8], which recommends to
firstly show an overview in order to let users understand which are the gen-
eral features of a dataset and then provide mechanisms for deeply exploring the
dataset details. We adapted the work by Auber et al. on Gosper treemaps [3]
for assigning a position and a shape to the abstract and non-geometrical data
generally contained in Linked Data sets. Our solution produces an interactive
map displaying all the instances of a dataset, organized in regions according
to their ontological classification. The map represents a ground layer on top of
which an atlas can be constructed by integrating multiple kinds of visualizations
and functionalities in order to bring out different aspects of a dataset.
In order to prove the effectiveness of the approach we applied it to some well-
known Linked Data sets such as DBpedia and LinkedMDB, and we performed
a user evaluation on the applications of both datasets.
1.1 Related Work
The large amount of work carried out for the visualization of Linked Data [8,
19] reflects the strong need of the Semantic Web community to provide users
with applications that let them easily access and explore Linked Data sources.
The most complete survey [8] about the approaches for visualising and exploring
Linked Data states that most of the existing tools i) do not provide an overview
of the dataset; ii) are designed only for expert users. In order to tackle and
solve those problems, different models and frameworks [6, 18, 17] for dynamically
creating and customizing data visualizations have been designed.
Other works employ information visualization techniques for statistical anal-
ysis. Cubeviz [20] is a visualization platform that allows to configure, display
and analyse statistical RDF data through different chart types. Linked Data
Linked Data Maps 3
Query Wizard [12] is an analysis tool that, given a SPARQL endpoint, allows
to search, filter and visualize resources using multiple diagrams. Payola [15] is
a web visualization framework that allows to run an analysis on a dataset and
then display the results with a set of visualization plugins. Although these tools
are very useful for obtaining interesting aggregated data, they neglect to provide
a complete representation of the dataset.
Several graph-based visualization tools with different features have been de-
veloped. The LODLive RDF browser [7] allows to manually expand the connec-
tions of a given resource and explore the resulting node-link diagram by consult-
ing relationships and attributes of the expanded resources. gFacet [11] combines
graph-based visualization and faceted filtering techniques for navigating a cer-
tain data source. The Linked Open Graph tool [5] provides a collaborative web
interface for examining RDF instance networks of different datasets. These ap-
plications support users in understanding the connections between instances but
they do not scale well with large datasets.
Other applications are focused in solving specific tasks such as Relfinder [10]
which reveals if and how two given resources are connected by visually showing
all the paths between them within the RDF graph they belong. Other tools
provide visualizations of restricted domains such as Spacetime [26] for spatio-
temporal data and Linked Geo Data Browser [24] for spatial data.
To summarize, the strong need for Linked Data exploration tools is still an
open issue in the Semantic Web community. We believe that the need becomes
even stronger when users approach a dataset for the first time, and that a novel
approach should be proposed in order to tackle the issue.
2 The Linked Data Maps Approach
The approach presented in this article is aimed to ease the comprehension of
the structure and content of Linked Data sources through interactive web ap-
plications developed around carefully designed visualizations. The main users
for which our applications are intended are those who want to develop tools on
or learn a specific Linked Data set even if are not very familiar with Semantic
Web technologies. Furthermore, casual users interested in the informative and
educational content of a dataset could benefit from it by using the atlas. Finally,
Linked Data experts that approach a new dataset for the first time could also
gain precious insights from this kind of application, which would quicken the
access to the target information or even help in finding anomalies in the data.
Our main objective is to provide those users with an entry point to the
dataset. By “entry point”, we mean an initial overview of the dataset that can be
queried and interacted with according to Shneiderman’s mantra, thus providing
an easy access to the information space. The initial overview should allow users
to perform the following high-level tasks: i) get a general idea about the way
the dataset is structured; ii) perceive its size; iii) compare different parts of it
in terms of both size and complexity. More specific tasks are also defined, to
characterize the user’s wish to get detailed information by interacting with the
4 F. Valsecchi, M. Abrate, C. Bacciu, M. Tesconi and A. Marchetti
visualization space: i) lookup or locate an instance; ii) consult its properties; iii)
browse the list of its connections; iv) inspect to find the location of instances
to which it is connected; v) discover which are the classes to which it is more
connected; vi) compare its connections with the ones of other instances; vii)
locate a class.
Our solution defines a pipeline that transforms an RDF graph into a map
as a result of two steps: data abstraction and spatialization. The map is then
visualized in a web application allowing to query and explore it (Figure 1).
V
A S
I
RDF Graph Compound Map Data UI
Network
Fig. 1. The Linked Data Maps approach. First the RDF Graph is transformed into a
Compound Network (A - Data Abstraction step), then the network is used to compute
the Map Data (S - Spatialization step). Finally, these data are visualized (V) in a web
application from which the user can interact (I).
2.1 Data Abstraction
As discussed in [25], an RDF graph (including its ontology) may be seen as a
compound network. Also known as multi-level network, a compound network is
a combination of a network and a tree, where the latter is seen as an organizing
structure for the former2 . Hierarchical ontologies are often the base structure of
Linked Data sets [8] as in the case of DBpedia, Geonames, Yago and Freebase.
However, other datasets such as LinkedMDB, Lexvo and Wordnet RDF are
organized as forests (i.e., undirected acyclic graphs whose connected components
are rooted trees). We deal with such structures by extending our approach to
handle common cases of ontologies: i) proper forests composed by n trees; ii)
forests comprising only one tree and iii) the degenerate case of a forest in which
there are n isolated classes. As shown in Figure 2, the forest is defined by class
nodes (i.e., ontological classes) while the graph is composed by instance nodes
(i.e., distinct URIs found as subjects or objects of RDF triples) that represent
the leaves of the forest trees. Three kinds of links are considered: vocabulary links
(VL) are derived from the ontology (i.e., rdfs:subClassOf property), relationship
links express the connections between instance nodes (e.g., birthPlace between
2
Recently, many approaches for the scalable drawing of networks have used this kind
of abstraction [2, 13]. Our approach uses the ontological classes instead of clusters
to play the role of organizing structures.
Linked Data Maps 5
class VL
nodes
TL
instance
nodes RL
Fig. 2. A graph and an associated forest define the extended compound network. In our
case, it is composed by class nodes, instance nodes, vocabulary links (VLs), relationship
links (RLs) and type links (TLs).
Galileo and Pisa) and type links (TL) define the membership of an instance to a
certain class (i.e., rdf:type). Table 1 shows some popular Linked Data sets and
the amount of nodes that would compose their compound network.
In general, an instance node could be a member of n classes, but, for the
dataset to be compatible with the Linked Data Maps approach, an instance
node can only be connected to compatible class nodes through a TL. Two class
nodes are compatible if they belong to the same tree branch of a connected
component of the forest (e.g., a Scientist is a Person). It is important not to
violate this compatibility constraint because otherwise instance nodes would be
connected to distinct conflicting class nodes, and consequently would have to be
displaced simultaneously in more than one region (e.g., Leonardo Da Vinci is
both a Scientist and a Painter ). In order to handle the missing types issue [21],
instance nodes without a corresponding class are allowed not to have any TLs.
Dataset Geonames DBpedia LinkedMDB Wordnet Lexvo
Class Nodes 7 721 51 5 5
Instance Nodes 8.3M 4.7M 694.400 647.215 128.945
Table 1. The amount of classes and instances of some well-known Linked Data sets
compatible with the Linked Data Maps approach.
2.2 Spatialization
As described in [25], the spatialization process adopted for constructing the
visualizations is based on the work of Auber et al. on Gosper treemaps [3].
Treemaps [14] efficiently use the available space for compactly displaying complex
trees by representing nodes as a hierarchy of regions contained into one another.
We choose to use Gosper treemaps because they strongly resemble islands, and
therefore support users in intuitively reading the generated geographic-like maps
as they would with traditional ones. We generalized the approach in order to
visualize forests: in the resulting map, each tree of the forest is depicted as a
6 F. Valsecchi, M. Abrate, C. Bacciu, M. Tesconi and A. Marchetti
distinct treemap, and thus a distinct island. A collision detection and a force-
directed graph algorithm are jointly adopted in order to obtain a layout in which
islands are displaced without overlapping.
Gosper treemaps represent each leaf of a tree as a hexagonal tile having a
specific position within the visualization. Instance nodes belonging to the same
ontological class are placed together in the same region. By construction, the
adopted spatialization process shows the amount of instance nodes linked to a
certain ontological class with the area of the corresponding region.
Beyond assuring the compactness of regions, the Gosper treemap layout guar-
antees a great level of stability, which is useful if an updated version of the map
needs to be created, because the new version will not confuse the user with con-
siderable differences in the spatial arrangement. This is an ideal property for
Linked Data sets that are often published with yearly updates.
2.3 Visualization and Interaction
To render the map data in a way that allows users to get an overview, zoom
and filter the visualization and get details on demand, the approach includes the
realization of a web application, mainly constituted by three components:
1. Map. It firstly displays the overview of the dataset showing all the instances
and classes within it. Zoom and pan mechanisms allow users to filter out
certain regions, move within the map and focus their attention on specific
regions and instances. Initially, only the regions with a suitable size show the
label identifying their ontological class, while the others are automatically
loaded during a zoom action. A metaphor for portraying instances as cities
have been introduced, in order to support users to get orientation within the
map and get a feel about the content of a certain region. Clicking an instance
on the map triggers the loading of its properties both in the map itself and
in the right-side infobox. All the instances connected to the selected one are
highlighted on the map as red tiles linked by red lines.
2. Infobox. It comprises all the RDF triples in which a selected instance is
involved. In particular, classes, data properties, incoming and outgoing re-
lations are organized in a user-friendly way for helping users in the consul-
tation. Furthermore, by clicking on an outgoing or incoming property, the
relative content is loaded both on the map and in the infobox, allowing the
dataset navigation.
3. Search Box. The search functionality allows to perform a text search for a
specific instance, show it on the map along with its connections, and get its
properties loaded in the infobox.
Multiple aspects of a dataset can be displayed on top of the main visualization
as additional thematic maps. Each region may be colored to represent for ex-
ample: the depth of the corresponding class in the forest, the density (i.e., a
normalization on the number of instances) of RDF triples, the density of the
object properties, or the density of data properties describing the instances. The
layers menu (on the top left) allows to switch between different thematic maps.
Linked Data Maps 7
3 Evaluation
We applied our approach to two well-known Linked Data sets: DBpedia[4] and
LinkedMDB[9], very different in size (Table 1), structure and content. DBpedia
is a cross-domain dataset with a hierarchical tree as its main ontology, while
LinkedMDB is a film industry dataset with an ontology structured as a col-
lection of disconnected classes. Both visual representations (Figure 3 and 4)
reflect the respective dataset structure. The tree of DBpedia, rooted on the
class “Thing”, is represented by one island composed by several regions, them-
selves partitioned in many hierarchical sub-divisions. An additional island has
been added for containing instances without a class [21]. Instead, the discon-
nected classes of LinkedMDB produce a completely fragmented representation
displayed as an archipelago of plain islands, since a single root is not defined.
3.1 Setup
The previous version of the application [25] had been preliminarily tested with
a very limited number of users. For this work, we carried out a more extensive
user evaluation with 19 users, collecting both qualitative and quantitative data.
Since the intended users are people wanting to approach or to better understand
a Linked Data set, all the testers we chose have a computer science background
(no lay users were recruited). The 19 participants were 12 males and 7 females
and they had different age ranges: 5 were between 20 and 30, 7 between 30 and
40 and 7 above 40.
Half of the people were presented the DBpedia application, while the other
half the LinkedMDB one. At the beginning of the session with each user, we
asked them to read a short description about the dataset3 . Then, we gave them
some time (5 to 10 minutes) for freely interacting with the application. Finally,
they had to fill out a questionnaire. To answer some of the questions they had to
perform some tasks using the application. The questionnaire has been structured
in four distinct parts for evaluating different aspects of the visualizations and
the applications.
1. Free interaction. Three questions have been defined for verifying whether
the main features (e.g., zoom and pan, search, and click on the map) of
the interface had been discovered and used by testers during the initial free
interaction phase.
3
For instance, in the case of DBpedia we used the following description: DBpedia
is a large collection of structured data automatically extracted from Wikipedia. It
contains a lot of resources (e.g. “Galileo Galilei”, “Pisa”, “The Beatles”, “Divine
Comedy”) organized in classes (e.g. “Person”, “City”, “Band”, “Book”). Classes
are connected together, forming a hierarchical structure from generic to specific. For
example, “FootballPlayer” is a more specific class than “Athlete”, which itself is more
specific than “Person”. Resources are also connected to each other (e.g. “Pisa” is
the place of birth of “Galileo Galilei”).
8 F. Valsecchi, M. Abrate, C. Bacciu, M. Tesconi and A. Marchetti
Fig. 3. A screenshot of the DBpedia application, available online at http://wafi.iit.
cnr.it/lod/dbpedia/atlas. The code is open source and hosted on GitHub (https:
//github.com/fabiovalse/dbpedia_atlas). The map, composed by a main island and
a smaller one containing the untyped instances, reflects the hierarchical structure of
the ontological tree at the base of DBpedia.
Fig. 4. A screenshot of the LinkedMDB application, available online at http://wafi.
iit.cnr.it/lod/linkedmdb/atlas. The map, composed by an archipelago of islands,
depicts the structure of the disconnected classes forming the ontology of LinkedMDB.
Linked Data Maps 9
Fig. 5. Thematic maps of DBpedia and LinkedMDB, showing the density of object
properties of each class (the darker, the more dense). By inspecting the maps, it can
be seen that the most dense classes are Soccer manager, Horse trainer and Jockey for
DBpedia and Film for LinkedMDB. The maps can also be compared in size since they
share the same scale.
Fig. 6. Thematic maps of DBpedia and LinkedMDB, showing the depth of the classes
in their ontology (the darker, the deeper). DBpedia has an island composed by several
hierarchical levels, while LinkedMDB has an archipelago of totally flat islands. By
inspecting the map of DBpedia, it can be seen that the deepest level of the ontology
corresponds to the small Diocese class (top right).
2. Tasks. Eleven tasks have been presented to users for testing if the applica-
tion is useful for: i) visually identifying which are the main regions within
a map; ii) looking up and locating resources; iii) recognizing which are the
connections between resources, and iv) understanding how resources are cat-
egorized.
10 F. Valsecchi, M. Abrate, C. Bacciu, M. Tesconi and A. Marchetti
After they finished each task, users have been asked to state whether it was
easy to solve. We adopted a scale of 5 balanced values (i.e., really difficult,
difficult, neither easy nor difficult, easy and really easy) ranging from 1 to 5
for scoring the results of each task.
3. Comprehension. Three questions have been asked for measuring whether the
users had grasped the main aspects of the visualizations (e.g., the meaning
of regions and their size).
4. Final comments. Four questions have been finally asked for understanding
whether the users thought the application was aesthetically pleasing, easy
to use, useful and/or self-explanatory. We used a traditional Likert scale
[16] ranging from 1 to 5 for scoring the answers of the users about the
aforementioned properties.
3.2 Results
The results of the test show that in the case of DBpedia the 80% of the users
autonomously discovered they could use the search feature and click on the map,
while 70% the zoom and pan behaviour. In the case of LinkedMDB, all the users
discovered they could click on the map, the 88% use the search function and the
75% of them used the zoom and pan mechanism. By taking into account those
results, now we think that the application requires a clearer way of showing
that the zoom and pan feature is available. The plus/minus zoom buttons often
included in web interactive maps could be a solution.
The study produced very promising results (Table 2 and 3) and also gave
directions for improving the strength of the application in communicating the
dataset characteristics. The mean score of every task is above average for both
DBpedia and LinkedMDB. One of the main aspects that came out from the
study is the need for a closer interaction between the visualization of the map
and the infobox. In fact, by observing the users when executing the tasks, we
noticed that some are more inclined to navigate the map, while others prefer to
just read the infobox.
All the users understood that the size of a region is determined by the amount
of resources contained within it, while only a few of them really perceived the
complexity of its hierarchical sub-structure. Almost all the users agreed on the
aesthetic, ease and usefulness of the application while only a part of them thinks
that it is self-explanatory, suggesting that a more intuitive user interface can be
envisaged. A user suggested to add an introductory tutorial for explaining the
main features the first time the application is accessed.
Linked Data Maps 11
Questions % Avg
Have you used the pan & zoom feature? 70% -
Free
Have you used the search feature? 80% -
Interaction
Have you clicked on the map? 80% -
Which is the biggest class in the dataset? 90% 3.6
Which are the main classes? Try to explain your answer. 100% 4.0
Where is the resource “The Matrix”? 100% 4.2
Which are the resources connected to “The Matrix”? 100% 4.2
Where is the resource “The Beatles”? 100% 4.2
Tasks Which are the resources connected to “The Beatles”? 100% 4.3
Is “Alan Turing” directly connected to “Noam Chomsky”? 90% 3.7
Is “Divine Comedy” directly connected to “Dante Alighieri”? 90% 3.5
Is “Earth” directly connected to a resource classified as “Place”? 70% 3.4
Is “Crow” directly connected to a resource classified as “Food”? 80% 3.9
Which are the classes of the resource “Danube”? 100% 3.7
Some regions are bigger than others because:
1) they contain more resources;
2) they contain more classes; 100% -
Comprehension 3) they are deeper in the hierarchy;
4) they have more connections.
Does region “Agent” seem more complex than “Place”?
1) Yes, “Agent” is more complex;
2) No, “Place” is more complex; 20% -
3) They are of similar complexity;
4) I don’t know.
Does region “CareerStation” seem more complex than “Place”?
1) Yes, “CareerStation” is more complex;
2) No, “Place” is more complex; 90% -
3) They are of similar complexity;
4) I don’t know.
The map is aesthetically pleasing - 4.1
Final The map is easy to use - 3.4
Comments The map is useful - 3.6
The interface is self-explanatory - 3.0
Table 2. The overall result of each question of the survey about DBpedia grouped by
category. The percentage of correct answers and the average score reported by users
are shown in the last two columns. Scores range on a scale from 1 to 5 (from really
difficult to really easy in the case of the tasks, while from totally disagree to totally
agree in the case of the final comments).
12 F. Valsecchi, M. Abrate, C. Bacciu, M. Tesconi and A. Marchetti
Questions % Avg
Have you used the pan & zoom feature? 78% -
Free
Have you used the search feature? 89% -
Interaction
Have you clicked on the map? 100% -
Which is the biggest class in the dataset? 88% 3.7
Which are the main classes? 67% 3.8
Where is the resource “The Matrix”? 100% 4.6
Which are the resources connected to “The Matrix”? 78% 3.9
Tasks Where is the resource “Sidney Lumet”
89% 4.3
(the film director)?
Which are the resources connected to “Sidney Lumet”
77% 4.4
(the film director)?
Is “Blade Runner” directly connected to
100% 3.8
“Rutger Hauer”?
Is “Marcello Mastroianni” directly connected to
78% 3.2
“Claudia Cardinale”?
Is “Christmas (Film Subject)” directly connected
100% 3.9
to a resource classified as “Actor”?
Some regions are bigger than others because:
1) they contain more resources;
2) they contain more classes; 89% -
Comprehension 3) they are deeper in the hierarchy
4) they have more connections.
Does region “Film” seem more complex than “Actor”?
1) Yes, “Film” is more complex;
2) No, “Actor” is more complex; 44% -
3) They are of similar complexity;
4) I don’t know.
Does region “Performance” seem more complex than “Film”?
1) Yes, “Performance” is more complex;
2) No, “Film” is more complex; 22% -
3) They are of similar complexity;
4) I don’t know.
The map is aesthetically pleasing - 3.8
Final The map is easy to use - 3.6
Comments The map is useful - 3.9
The interface is self-explanatory - 2.9
Table 3. The overall result of each question of the survey about LinkedMDB grouped
by category. The percentage of correct answers and the average score reported by users
are shown in the last two columns. Scores range on a scale from 1 to 5 (from really
difficult to really easy in the case of the tasks, while from totally disagree to totally
agree in the case of the final comments).
4 Conclusion and Future Works
In this paper, we presented an approach for visualizing and exploring Linked
Data sets based on information visualization and cartographic techniques. We
Linked Data Maps 13
extended the work started in [25] to handle datasets organized by forest-based
ontologies. We applied the approach to DBpedia and LinkedMDB, implement-
ing two web applications available on-line. We presented the outcome of the
user evaluation that shows promising results, but also some weak points. Be-
side addressing these flaws, next studies will be focused on other improvements.
The zoom behaviour could be strengthened by progressively loading the most
important “cities” (i.e., instances), selected according to a ranking factor. This
score could be based on the number of links to which a node is connected (i.e.,
in-degree, out-degree or degree). Finally, a similarity measure between instances
could be introduced. This aspect could be very useful for further organizing re-
gions in clusters of similar instances. A layout strategy completely based on such
a measure could also solve the incompatible classes problem described in Section
2.1, making it possible to handle an even wider range of datasets.
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