=Paper=
{{Paper
|id=Vol-2790/paper32
|storemode=property
|title=
Adapting the LodView RDF Browser for Navigation over the Linguistic Linked Open Data Cloud in Russian and the Languages of Russia
|pdfUrl=https://ceur-ws.org/Vol-2790/paper32.pdf
|volume=Vol-2790
|authors=Alexander Kirilovich,Konstantin Nikolaev
|dblpUrl=https://dblp.org/rec/conf/rcdl/KirilovichN20
}}
==
Adapting the LodView RDF Browser for Navigation over the Linguistic Linked Open Data Cloud in Russian and the Languages of Russia
==
https://ceur-ws.org/Vol-2790/paper32.pdf
Adapting the LodView RDF Browser for Navigation over
the Linguistic Linked Open Data Cloud in Russian and
the Languages of Russia
Konstantin Nikolaev1 and Alexander Kirillovich1,2
1Kazan Federal University, Kazan, Russia
2Joint Supercomputer Center of the Russian Academy of Sciences, Kazan, Russia
konnikolaeff@yandex.ru, alik.kirillovich@gmail.com
Abstract. This paper is dedicated to using of LodView RDF browser for naviga-
tion on the Linguistic Linked Open Data cloud in Russian and languages of Rus-
sia. We reveal several limitations of LodView, that prevents its using for this
purpose. These limitations are: 1) resolution of Cyrillic URIs; 2) Cyrillic URIs in
Turtle representations of resources; 3) support for Cyrillic literals; 4) support for
URIs with IDs of fragments; 5) human-readable URLs for RDF representations
of resources; 6) deployment of embedded resources. We updated the LodView
for fix the recovered limitations.
Keywords: LodView, RDF browser, Linked Open Data, Linguistic Linked
Open Data.
1 Introduction
Linked Open Data (LOD) is a powerful and flexible tool for sharing and distributing
information. The task of LOD is to establish a link between data and its meaning, which
allows search engines to move from a keyword-based approach to one based on the
meaning of the search query. In addition, research based on the semantic approach to
information allows us to identify more significant characteristics of the text, as opposed
to a simple syntactic approach. LOD is most actively used in medicine (in the task of
establishing links between diseases and possible treatments), in the scientific literature
in the task of structuring citations in a huge number of articles published on the web. In
addition, LOD finds its active application in problems of computational linguistics: in
particular, problems of semantic parsing of texts and problems of generating texts in
natural language. Having a prearranged LOD cloud based on the subject of the text
allows one to analyze the contexts of words in the text more correctly and suggest the
most likely phrases in the generated texts.
On the other hand, it is necessary to develop high-quality tools that allow ordinary
users or researchers from other areas that are not related to LOD development to fully
interact, research and analyze the data presented in LOD sets. Visual data analysis al-
lows one to combine the capabilities of a computer and human research abilities, to
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
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identify new patterns in complex interconnected data. The steps involved in data visu-
alization are as follows:
Defining data sources
Getting and extracting data
Data modeling
Data preparation
Visual preparation
Data visualization
In most cases, all stages except the last one are performed by the dataset creators, since
the visualization tools that perform data preparation and visual data preparation cannot
be universal for different types and structures of datasets.
There are many ways to visualize data, ranging from basic visualization using histo-
grams, charts of various types (point, column, circle, etc.), to visualization using graphs.
In addition, these types of visualizations are often extended by interaction capabilities.
Yi et al. [1] provide a taxonomy of ways to interact with displayed data that can be
applied in various ways.
Selection of items
Exploring the content of a specific element
Reconfigure: meaning the user can change the location of elements in the visualiza-
tion field
Encode: changing the appearance of elements
Abstract: changing the level of abstraction of information as necessary
Filter: ability to custom filter the currently displayed data.
Connect: display and hide links between elements.
These visualization methods with interaction capabilities are suitable for small da-
tasets and are often used in such tasks. However, when visualizing large amounts of
data, other methods that take into account the features of big data are necessary. The
following are some of the techniques used by these methods:
Data reduction: a technique based on the use of approximation techniques, which are
used to calculate abstract datasets. Many of them are based on sampling, filtering
([2,3]) and aggregation ([4,5])
Hierarchical data exploration: displaying large datasets as layers with different levels
of detail with the ability to navigate between layers ([4,6])
Progressive data visualization-displaying data in small portions, on the principle of
"as fast as possible", a new portion is loaded by any user action ([7,8,9])
Data structures and indexes: building indexes or dividing a dataset into subgroups
according to a specific algorithm (HETree in [4], VisTrees in [10], Hashedcubes in
[11] and RawVis in [12]).
Now that we have considered the main methods of data visualization, we need to
provide types and examples of existing tools for data visualization. They can be divided
358
into several groups based on the approach to visualization. For each group, we will give
some of the most relevant examples.
Browsers and exploratory tools.
Tools of this type are the simplest and earliest implementations of technologies for data
visualization. Usually, the data in such tools is presented in a form similar to the classic
web browser, but with the addition of functionality for navigation and displaying LD
resources. Existing solutions include the following: Haystack[13], Visor,
PepeSearch[14], RDFSurveyor. Some solutions use the faceted search technique
(/facet[15], BrowseRDF, Explorator[16], Sewelis, Sparklis, tFacet и другие).
Tools using multiple visualization types.
These visualization tools allow the user to choose the desired way of data visualization,
such as diagrams, tables, Euler circles, maps, graphs, and so on. Here are a few tools
that have the most complete set of visualization capabilities: Rhizomer[17], Payola,
SynopsViz, VisWisard[18], YDS.
Graph-based visualization tools.
Tools in this category rely on representing LD as graphs. Many of the tools in the pre-
vious group support the graph representation, so here we present the tools for which
graph representation is the main one: LodLive[19], Aemoo, LODmilla[20], graph-
Visdb, RDF4U, H-BOLD, Tarsier etc.
Domain, vocabulary-specific, and device-oriented visualization tools.
This type of tool is intended for a specific type of data. Such tools are not of particular
interest in this article, so we will point out just a few examples: LinkedGeoData,
Spacetime, Facete.
Ontology visualization tools.
Data visualization tools have been actively developed over the past 20 years. In this
section, we should mention tools that are suitable for different LD, rather than for one
specific data type. The following services are the most flexible: OWLPropViz (addon
for Protégé), OntoStudio, OWLGrEd (addon for Protégé), SOFA, Ontodia. Besides,
OntoSphere [21], Onto3DViz [22], and MVOV [23] implement three-dimensional data
visualization.
2 LodView
As part of our project related to linguistic LOD, there was a need for a tool that will
allow us to display the contents of the Russian linguistic LOD set. LodView[24] was
chosen as the optimal one in terms of functionality and simplicity, since it supports
displaying fields of various resources in text format, and linguistic data assumes a plain
359
text format. In addition, LodView is based on hierarchical data exploration, meaning it
displays a list of objects related to the current one, making it possible to navigate from
one object to another. Fig. 1 shows an example of using LodView to visualize the Lon-
don concept from dbpedia.org.
Fig. 1. Example of LodView usage in dbpedia.org data visualization
However, LodView requires some improvements, which will be given in the next
section.
3 Necessary Improvements of the LodView
1. Resolution of Cyrillic URLs. Datasets from the Russian-language LLOD can use
URIs containing Cyrillic characters. For example, in the RuThes Cloud set, the URI
used to denote the lexical unit "машина" is . However, on some configurations, resolving the Cyrillic URIs
in LodView leads to an error (see Figure 2). We must add support for URIs contain-
ing Cyrillic characters to LodView.
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Fig. 2. Error while resolving a link containing Cyrillic literals
2. Cyrillic URIs in the Turtle representations of resources. In a machine-readable
representation of a resource in the Turtle serialization format, Cyrillic URIs are en-
coded. Figure 3A shows a fragment of the Turtle representation of the
"Автомобиль" concept with encoded URIs. Representing URIs in encoded form
makes it difficult to perceive them. In the Turtle representation, URIs must be rep-
resented in decoded form (for example, as in Figure 3B)
Fig. 3. Fragment of a Turtle representation of the "Car" concept from the RuThes Cloud re-
source with encoded and decoded URIs
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3. Support for Cyrillic literals. On some configurations, literals are represented with
a broken encoding in the machine-readable representation of the resource. This hap-
pens even if the dataset has Unicode encoding. Figure 4A shows a Turtle represen-
tation of the RuThes Cloud resource with a broken encoding. It is necessary to dis-
play Cyrillic literals correctly on all configurations (as in Figure 4B).
Fig. 4. Fragment of the Turtle representation of the RuThes Cloud resource
4. Support for URIs with fragment IDs. Datasets from the LLOD cloud can use URIs
that contain Cyrillic fragment IDs. These URIs are used to indicate a resource that
is closely related to some other resource. For example, in the RDF representation of
the Princeton WordNet, the URI used to denote the lexical unit "cat" is , and the URI used to indicate the canonical form
of this lexical unit is . In this case, the URI for the canonical form of the lexical unit "cat" con-
sists of the URI of this lexical unit, to which the ID of the fragment "#Canoni-
calForm" is added.
However, LodView does not support URIs containing the IDs of the fragment. So,
for example, when resolving the URI , LodView returns a representation not of the resource that is des-
ignated by this URI, but of the resource that is designated by the URI . This problem occurs because of the HTTP Pro-
tocol: when sending a request, only a fragment of the URI is sent without the frag-
ment ID. To solve this problem, when resolving any URI, LodView must return a
representation not only of the resource designated by this URI, but also of all re-
sources whose URI is formed by combining the URI of the source resource and some
362
fragment ID. For example, it is necessary that when resolving the URI , LodView returned not only a representation of
the resource denoted by this URI, but also of the resource denoted by the URI
.
5. Human-readable URLs for the RDF representation of resources. LodView im-
plements the negation content principle in the process of resolving URIs. In accord-
ance with this principle, when a user requests the URI of a certain resource, LodView
redirects the user to the URL where the web page with human-readable resource
representation is located. When a resource URI is requested by a software agent,
LodView redirects the agent to a URL with a machine representation of the resource.
URLs for representing resources as web pages have a user-friendly format: < resource
URI>.html. For example, a web page for a resource with the
URI has the
URL. However, URLs
for RDF representations of resources are not easy to read. For example, a Turtle
representation for a resource with has the URL. Machine-readable RDF resource rep-
resentations must have the form like .ttl, .n3 etc. For
example, the Turtle representation of resource must have the URL.
6. The unfolding of the embedded resources. On a web page with a resource view,
the resource properties are presented as a table. The first column contains the prop-
erty name, and the second column contains its value.
If the property value is the URI of another resource, in order to find out the properties
of this resource, the user must follow the link of the resource. The system must allow
the user to expand embedded resources so that they can see the properties of these
embedded resources without leaving the source page.
If the property value is an anonymous resource (blank node), the property value is
the surrogate ID of this anonymous resource, and the description of the anonymous
resource itself is located at the bottom of the page. Similarly, the system should allow
the user to deploy an anonymous resource to see its properties "in place", without
scrolling the page.
4 Implementation of Improvements
At the moment, we have fully implemented improvements 1-3 and 5, as well as partially
implemented improvement 6.
Problem of resolving Cyrillic URIs (improvement №1).
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After analyzing the source code of LodView, we found that this problem is related to
the fact that the URI text is represented in the SPARQLEndPoint class in Western ISO-
8859-1 encoding. This problem was solved by adding a single line of code to the spec-
ified class that recodes the URI'I from ISO-8859-1 to Unicode:
IRI = new String (URI.getBytes ("ISO-8859-1"), "UTF-8");
After we solved this problem, LodView began to correctly resolve Cyrillic URLs
and return a web page displaying the corresponding resource when accessing them.
However, after this, we found a new problem: the resource URI is also displayed incor-
rectly on the returned web page (see Figure 5).
Fig. 5. Incorrect display of the resource URI
We solved this new problem by performing a similar recoding in the code of the
corresponding web page (resource.jsp).
Problem with displaying Cyrillic literals (improvement №3).
This problem was solved in the same way as the previous one, using a similar recod-
ing.
Problem with encoded URIs (improvement №2).
To solve this problem, we have made changes to the ResourceBuilder class.
Problem with fragment identifiers (improvement №4).
In the course of solving this problem, we encountered that it is not possible to cor-
rectly send the # symbol to the server, which is why the server displays the basic version
of the resource, and not the requested one. The solution to this problem (although not
the most elegant) is to replace the # symbol with another character (rarely used in nor-
mal texts and URLS) on the client side and decode it to # on the server side. This way,
364
we can correctly pass the address of the resource we are looking for to the server (Fig.
6).
Fig. 6. Blank nodes representation in the resource with fragment identifier.
The disadvantage of this approach is that we can only go to a resource with the frag-
ment ID from another resource. If we enter an address in the address bar, the resource will be displayed instead. For this reason, in
the future we will have to change the structure of the web page that displays the resource
content and include related resources with different fragment IDs.
Problem with human-readable URLS (improvement №5).
To solve this problem, we added support for such URIs by making changes to the
ResourceController class.
The problem with the deployment of the embedded resources (improvement №6).
This task is completely solved and consists of two changes. For easy viewing of
embedded resources, we added the “Expand" button, which displays an iframe contain-
ing a page with a web view of the embedded resource (Fig. 7).
Displaying fields in blank nodes was implemented using an existing block with
blank nodes. Part of its content associated with a resource that is present in the main
365
block of the web page is moved to the parent element of this resource. Thus, detailed
information about the contents of the blank node is displayed directly below it (Fig. 6).
Fig. 7. Example of deploying an embedded resource using an iframe
5 Conclusion
This article provides an overview of the main principles, methods, and existing solu-
tions for LD visualization, as well as ways to improve the web application for visualiz-
ing resources from triplet stores, aimed at correctly displaying Russian-language re-
sources (or any other ones that use non-Latin characters).
These improvements include: 1) Resolution of Cyrillic URIs; 2) Cyrillic URIs in
Turtle representations of resources; 3) Support for Cyrillic literals; 4) support for URIs
with IDs of fragments; 5) Human-readable URLs for RDF representations of resources;
6) Deployment of embedded resources.
At the moment, we have fully implemented revisions #1-3 and #5-6, and partially
implemented revision #4. The implementation of these improvements is the main con-
tribution of this article.
These improvements will be offered as a fork to the LodView project and will be
used as the main resource for viewing the contents of triplet repositories within our
project.
Acknowledgements. The work was initiated for navigation over LLOD cloud by
K. Nikolaev and A. Kirillovich at KFU, where was funded by Russian Science Foun-
dation according to the research project no. 19-71-10056, and then adapted for naviga-
tion over specialized LaTeX-based mathematical collections by A. Kirillovich at JSC
RAS.
366
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