HANDS-ON- SESSION - Challenges and Opportunities with Big Linked Data Visualization - tutorial @ISWC 2018 A book on the topic published by the author is "Linked Data Visualization: Techniques, Tools and Big Data" Laura Po, Nikos Bikakis, Federico Desimoni & George Papastefanatos Synthesis Lectures on Data, Semantics and Knowledge Morgan & Claypool, 2020 ISBN: 9781681737256 | 9781681737263 (ebook) DOI: 10.2200/S00967ED1V01Y201911WBE019 Morgan & Claypool: https://www.morganclaypool.com/doi/abs/10.2200/S00967ED1V01Y201911WBE019 Homepage: http://www.linkeddatavisualization.com

1. LINKED DATA
VISUALIZATION SYSTEMS
HANDs-ON-SESSION

2. OUR CHOICE
This hands-on session takes into consideration only some LOD tools
It is not an exhaustive survey of LOD tools
• tools available online
• tools with some demo or demo dataset
• tools that show different aspect of visualization of LOD

3. Disco
Linked Data browsers
VizBoard
Rhizomer
SemLens
Linked Data Exploration Systems
LOD Viewer
Payola
Linked Data Graph Tools
Tim Berners-Lee coined the term Linked Data
Aesthetics in Interface Design for LD [Mazumdar 2015]
SynopsisViz
H-BOLD
Lodlive
LODWheel
Balloon synopsis
LDVizWiz
Fenfire
Gephi
graphVizdb
LODeX
Vis Wizard
RelFinder
ViziQuer
Ontology Visualization Systems
CropCircles FlexViz GLOW
OntoGraf
OntoTrix
OWLViz
VOWL 2
Explorator
Marbles
Tabulator
gFacet
EVOLUTION OVER TIME
Aemoo

4. CHECK YOUR WIFI CONNECTION
Wi-Fi Access Troubleshooting
- Restart the browser
- Forget the Wi-Fi network, and connect again to the Conference network.
Just as a reminder the username and password for the Conferene network is:
conf8690/conf8690 (same username and password).
One (or both) of these steps resolved connectivity issues for most.
Save Wi-fi bandwidth
Please, turn off applications such as email client, Skype, Slack, shared drives
Please, refrain from downloading large media files

5. GENERIC LINKED DATA
VISUALIZATION SYSTEMS

6. Balloon synopsis
• Balloon project aim is to provide Linked Data as a Service
• Components:
• Overflight queries many endpoints searching for equivalence (owl:sameAs)
and for creating a bird’s eye view over all the datasets
• Fusion  query rewriting service for searching data over all the datasets
• Commonalities  extension of Fusion that crawls more predicates (rdf:type,
rdfs:subClassOf, …)
• Synopsis  graphical visualizer and explorer tool; it can be embedded in any
web application.
• Web Site: http://schlegel.github.io/balloon/index.html
[Schlegel 2014]

7. TRY BY YOURSELF
• http://schlegel.github.io/balloon/balloon-synopsis.html
• How many people live in Germany?
1. In «Demonstration» section click on «Passau»
2. Order the properties alphabetically
3. Scroll down until you get country property and
click on it
4. Scroll down until you get populationTotal
property and there’s the result

8. Rhizomer
• Web application that facilitates publishing and also exploring Semantic Web
data (and particularly Linked Data).
• It explores data in 3 steps:
• Overview  What is a dataset about?
• Filter  View properties and values. Filter them. Pivot among entities through
relations
• Visualize  Visualisations declaratively associated to data
• Provides various graphical representations of data
• Web Site: http://rhizomik.net/html/
[Brunetti 2012]

9. TRY BY YOURSELF
• http://rhizomik.net/html/rhizomer/
• Search information about Lucy Gordon
1. Open Rhizomer DBPedia from
«Example» section
2. Click on Agent
3. Filter Agent by
Birth Date = 1980 and
Death Date = 2009
4. Click on Lucy Gordon
for more information

10. SynopsisViz
• Web application for visualizing linked data
• Dataset can be uploaded from local disk from rdf files (.rdf, .n3, .nt, …)
• Provides both general dataset information and instance level view through
various kinds of visualization (textual and graphical)
• Instance filtering can be done only at property level (extract only instances
with that property)
• Graphical representation visualize some aggregated data
• Web page: https://www.w3.org/2001/sw/wiki/Rdf:SynopsViz
• Visual scalability - SynopsViz adopts approximation techniques (i.e.,
sampling/filtering, aggregation).
[Bikakis 2014] [Bikakis 2017]

11. TRY BY YOURSELF
• http://synopsviz.imis.athena-innovation.gr/
• Draw a chart based on rgbCoordinate of the elements form the class Colour
1. From «Classes» select «Colour»
2. Select rgb properties
• rgbCoordinateRed
• rgbCoordinateBlue
• rgbCoordinateGreen
3. In «Visualization» menu
select «Chart»

12. VisWizard
• Web application created for exploring linked data and for visualizing
datasets
• At first, the user has to select a dataset from a predetermined list and insert
some keyword inside the search bar. Then the tool will query that dataset’s
endpoint end extract all the information about the requested item
• The retrieved information will be displayed in a tabular way where it will be
possible to add or remove columns as user wishes
• Working application at: https://code.know-center.tugraz.at
[Tschinkel 2014]

13. TRY BY YOURSELF
• https://code.know-center.tugraz.at
• Search for Area code, lat and long of all cities that contains «Modena» in the name
1. Type «modena» in the «Search Linked
Data” bar, keep selected the “dbpedia”
dataset and start the search
2. All cities whose name contains Modena
around the world will be displayed
3. Remove «type» property column
4. Add «Lat», «Long» and «Area code»
property column
5. Click on «Area code» and select «hide
empty result»

14. AEMOO
• Unlike other tools it tries to analyze not only dbpedia dataset but also news
from google and tweets from twitter
• It provides only an instance level view with a singularity: the properties of
every object seem to be extracted from wikipedia (and not from dbpedia)
in an automatic way through Encyclopedic Knowledge Patterns (EKPs)
• Researches can start from an initial resource and then explore the relations
• The history of the exploration is shown at the bottom of the page
• Web application at: http://wit.istc.cnr.it/aemoo/

15. TRY BY YOURSELF
• http://wit.istc.cnr.it/aemoo/
• Who wrote films or TV series starring an australian shepherd?
1. Type «Australian shepherd» in the search
box (select the entry with an abstract)
2. Film and TV series are inside the «Work»
class so hover that node and a new pop-
up containing a list of works will appear
3. Hovering on a work will show the
correlation found in the wikipedia page
between the australian shepherd and that
work
4. Click on a work and search for the «Writer»
node
5. Do the same thing for the other two works
Hint: «Flash Forward» series doesn’t have a
writer node so it must be searched in
Wikipedia page

16. GRAPH BASED
VISUALIZATION SYSTEMS

17. graphVizdb
• In the online version, only two datasets can be loaded (DBLP, DBpedia
Person) but one at a time
• No general information about the datasets
• The entire dataset is depicted through a graph (the bigger is the dataset
and the wider is the graph)
• Each node is an instance and every edge is a property (datatype or object)
• Selecting an instance (node) will show its properties. This is the first step for
exploring the dataset
• Web Page: https://www.w3.org/2001/sw/wiki/GraphVizdb
[Bikakis 2015] [Bikakis 2016]

18. TRY BY YOURSELF
• http://83.212.97.26:8080/graphVizdb/
• Extract a graph containing information about Rafael Nadal
1. Change dataset and load
DBPedia Person
2. In Search tab type
«Rafael_Nadal»
3. Click on the resulting node
4. In Isolate tab select «Isolate
current node» and set
Isolation level = 1

19. LodLive
• LOD exploration following a user driven approach
• Default endpoint: DBPedia endpoint
• Search by keyword/ URI
• Limited list of endpoints that can be queried
• Graph visualization include only linked objects, rdf:type relation and inverse
relation. Literals can be found in a black tab visible after clicking the
document icon over the node
• Web Site: http://lodlive.it/
[Camarda 2012]

20. TRY BY YOURSELF
• http://en.lodlive.it/
• Who are the presenter of Top Gear U.S.?
Option 1 - Type
http://dbpedia.org/resource/Top_Gear_(U.S._TV_series) in
«Insert URI» text area
and click on «Start»
Option 2 – Select dbpedia datset in
«simple search» and insert
the following keywords «Top Gear»
2. Explore the properties (click on
the little circles) until you find the
«presenter» and click on them

21. RelFinder
• Focus on display relationships between objects
• At first the user must select at least two objects for which to find relations
• Then a graph will be built over those relations
• Graph construction is not instantaneous, it is created slowly allowing the user
to understand that many relations (it’s possible to skip the creation and see
the full graph immediatly)
• Web Site: http://www.visualdataweb.org/
[Heim 2010]

22. TRY BY YOURSELF
• http://www.visualdataweb.org/relfinder/relfinder.php
• Do the following films had the same producer? If yes, Who is he?
1. Add a node
2. Type the following one per node:
• The Avengers (2012)
• Captan America: The First Avenger
• Iron Man (film)
3. Click on «Find Relation»
4. Browse until finding producer property
and answer the question

23. VOWL
• A suite of tools for exploring the LOD and ontologies:
• WebVOWL  ontology visualization tool; it draws the schema of an ontology
after converting the ontology in a json file
• LD-VOWL  Linked Data visualization tool; it draws the schema of the dataset
after querying the dataset’s endpoint
• QueryVOWL  Linked Data qeurying tool; this tool helps the user to create visual
queries over linked data
• Web site: http://vowl.visualdataweb.org/
[Lohmann 2015]

24. QUERYVOWL - TRY BY YOURSELF
• http://vowl.visualdataweb.org/queryvowl/queryvowl.html#
• Search for agents born in 1950 and died before 2000
1. Type agent in the search bar and
select Agent class from Dbpedia
2. Select the property birth date
(then repeat with death date)
3. Double click on the object of the
property to set the filter
4. The result will appear at the bottom

25. LOG – Linked Open Graph
• Tool for consuming linked data from variuos sources
• Many endpoints were collected and can be queried by LOG searching for
keywords or URIs.
• Endpoints outside of LOG list can also be queried through a different form
• After seaching for a resource it will appear on the graph area and it will be
possible to explore his properties and other objects linked to it
• Possibility of saving the graph and embedding it to other applications
• Web page: https://log.disit.org/service/
[Bellini 2014]

26. TRY BY YOURSELF
• https://log.disit.org/service/
• When Roger Federer won the gold medal at the Olympics?
1. Select DBPedia dataset
2. Type «http://dbpedia.org/resource/Roger_Federer» in
URI textbox and start the research
3. When did he won the gold medal
at the Olympics?
4. The initial graph is a bit confusing
but filtering properties can be done
from «Type of relation» section
5. Search for dbo:goldMedalist
and see the result

27. H-BOLD
• Tool still under developement created for the exploration of the Semantic Web
• The main page presents a sortable list containing datasets and some generic
information about it (number of triples, classes, properties and instances)
• For each dataset it’s possible to visualize two distinct graph:
• Schema summary: complete schema of the dataset containing all the classes and
their connection
• Cluster summary: clusterized and expandible schema containing only top-level
classes and the classes chosen by the user
• A click on a node will show all the properties (datatype and object) of the selected
property
• Instance view system and SPARQL query generator will be implemented with further
developement
• Working application at: http://dbgroup.ing.unimo.it/hbold_bootstrap/
Po 2018

28. TRY BY YOURSELF
• http://dbgroup.ing.unimo.it/hbold_bootstrap/
• What about analyzing the structure of the «Alpine Ski Racers of Austria» dataset? How
many relations does the «Company» class have?
1. Search for «Alpine Ski Racers of Austria» using the search bar
2. Let’s give a look at the schema summary by clicking on the SS button
3. This dataset contains many classes and
the graph is very messy. Let’s clear it
4. Go back to the main page, search
again for the dataset and click on CS
button; now the graph is smaller and
we can explore it.
5. Click on Person, select «Company» and
click on the «Create Schema Summary»
button; now the graph shows all
relation of this class
6. We can go even further and analyze the
relations of «CompaniesEstablishedIn1924»
class so select that node and click on
expand; now the node «Person100007846»
has been added to the graph

29. HOW FAR MIGHT BE THE VISUALIZATIONS OF
THE SAME INSTANCE
USING DIFFERENT TOOLS?

30. FIND THE DIFFERENCES
LodLive

31. FIND THE DIFFERENCES
LOG – Linked Open Graph

32. FIND THE DIFFERENCES
QueryVOWL

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34. Thank you for the attention