=Paper=
{{Paper
|id=Vol-3365/paper1
|storemode=property
|title=Linked Data interfaces: a survey
|pdfUrl=https://ceur-ws.org/Vol-3365/paper1.pdf
|volume=Vol-3365
|authors=Eleonora Bernasconi,Miguel Ceriani,Massimo Mecella
|dblpUrl=https://dblp.org/rec/conf/ircdl/BernasconiCM23
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==Linked Data interfaces: a survey==
https://ceur-ws.org/Vol-3365/paper1.pdf
Linked Data interfaces: a survey
Eleonora Bernasconi1,* , Miguel Ceriani2 and Massimo Mecella1
1
Sapienza Università di Roma, Department of Computer, Control, and Management Engineering Antonio Ruberti (DIAG)
Via Ariosto, 25, 00185 Rome, Italy
2
Università degli Studi di Bari Aldo Moro, Department of Computer Science Via Edoardo Orabona, 4, 70125 Bari, Italy
Abstract
Cultural Heritage has become a considerable active area of application of linked data and semantic web
technologies. Visualizing and exploring the linked data produced to enhance cultural heritage is one of the
significant challenges in the research field of Digital Humanities. Therefore, in this survey, we describe
systems developed by the semantic web community in the Web of Linked Data context. We compared
twenty-eight interfaces that show both about generals that digital library contents. We classified them
by interaction paradigm, type of information displayed and complexity reduction strategies used.
Keywords
linked data interface, semantic web, digital library, knowledge graph interface, visualization tools
1. Introduction
Large amounts of cultural heritage linked data are available for research and public use [49,
50, 51, 52]. Visualizing and exploring the linked data produced to enhance cultural heritage
is one of the significant challenges in the research field of Digital Humanities. Therefore,
relevant literature is surveyed for relevant works concerning the visualization and exploration
of semantic data as knowledge graphs. We selected twenty-eight tools, considered significant to
represent the identified categories of classification. We have tagged the tools primarily for the
reference context: digital libraries or generic databases than we classified them according to the
type of interaction paradigm used, the type of information displayed and finally, the strategy
used to reduce the displayed information. The classification categories are listed in figure 1.
In figure 2 are listed the Linked Data (LD) interfaces to which the categories they belong have
been assigned. The first category (a) describes the interaction paradigm used, which can be a
tabular visualization, node-link visualization or visual query composition; the second category
(b) describes the type of information to be displayed: data, model or data to model (namely
schema extraction); the third category (c) describes the strategies used to reduce the displayed
information: navigational visualization, incremental visualization or summarized visualization;
the last category (d) describes the interfaces that deals with digital library contents.
19th IRCDL (The Conference on Information and Research science Connecting to Digital and Library science), February
23–24, 2023, Bari, Italy
*
Corresponding author.
$ bernasconi@diag.uniroma1.it (E. Bernasconi); miguel.ceriani@uniba.it (M. Ceriani); mecella@diag.uniroma1.it
(M. Mecella)
0000-0003-3142-3084 (E. Bernasconi); 0000-0002-5074-2112 (M. Ceriani); 0000-0002-9730-8882 (M. Mecella)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
Figure 1: Classification categories
2. Related work
This section introduces some previous work that surveyed and classified LD interfaces. We
have listed below some of these by year and description in table 1.
Table 1: Related work
work year description
S. Mazumdar et al. [46] 2014 user and system requirements of LD visualization
N. Marie et al. [40] 2014 survey of LD based exploration Systems
N. Bikakis et al. [39] 2016 exploration and visualization in the web of big LD
A. Dadzie et al. [42] 2017 visualisation of LD
Klímek, J. et al. [45] 2019 survey of tools for LD consumption
Desimoni, F. et al. [44] 2020 empirical evaluation of LD visualization tools.
Po, L. et al. [38] 2020 LD visualization: techniques, tools, and big data.
Hyvönen, E. [41] 2020 using the semantic web in digital humanities
Krommyda, M. et al. [47] 2020 visualization systems for linked datasets
In [39], the authors provide an overview of current systems and techniques for visualizing
LD but with a less detailed discussion. The authors identified six classification categories,
some independent of each other, while we focused on the three categories representing each
tool for LD visualization and exploration. In [41], semantic portals have been categorized
conceptually into three generations: portals for search and browsing, portals with tools for
distant reading and portals for serendipitous knowledge discovery. The authors focused more
on the purposes of use than connecting the uses with the primary interfaces’ characteristics.
[40] overviews 16 existing tools in three categories, classified according to 16 criteria. The first
Figure 2: Semantic web interfaces
category represents the LD browsers; the second represents domain-specific and cross-domain
recommenders; the third represents the exploratory search systems. In [46], the authors perform
a focus group examination. They aim to determine the users’ needs and system requirements
for visualizing LD using the dashboard approach. The authors highlight the heterogeneity of
LD and the need for highly customized visualizations for various kinds of LD. In [45], authors
have surveyed existing tooling for LD consumption for non-technical end-users and presented
general requirements for end-user LD platforms, encompassing a variety of topics, such as
dataset discovery and data manipulation. In [47], authors present the available techniques for
visualizing large linked datasets and the systems that implement them. They identify seven
categories of LD interfaces: semantic browsers; graph-based visualization systems; hierarchical
visualization systems; SPARQL endpoint visualization tools; facet browsers; query writers, and
schema extraction. Existing works tend to classify link data interfaces by usage purposes and
knowledge graph (KG) visualization manners, not categorizing them by characteristics common
to all LD exploration and visualization interfaces. For this reason, we propose the analysis of
some tools that are representative of each category that belongs to each of them with obviously
different specifications. We are talking about the interaction paradigm used, which includes
how the KG is shown to the user. The type of information displayed indicates what type of
data, models, or information conversion functions the specific interface being analyzed uses.
The strategy of reducing the information displayed that each interface uses to overcome the
problems of loading the multitude of information contained in the knowledge base that the
interfaces query. Below we will analyze each category identified, referring to the analysis of
tools that we believe are representative of the paradigm, information, and strategy described.
3. Visualization of semantic data
The extracted semantics can be extremely useful for exploring a corpus of documents, but they
are not fixed and homogeneous like a set of predefined metadata. Therefore, data models and
visual user interfaces need to deal with these complex and heterogeneous data. The semantic
web [1] and LD [3] efforts deal with data modelling, integration, and interaction of this kind of
data on the web. These efforts lately contributed to the emergence of KGs to organize complex
data-sets integrating multiple sources [10, 6]. In a visualization scenario, users are expected to be
interested in finding specific resources and exploring something exciting and valuable without
knowing what they are looking for until they identify it (serendipity effect). In this case, users
perform a sequence of operations (e.g., queries) in which each operation’s result determines
the next operation’s formulation. In this context, many user interfaces for visualization and
exploration of KGs exist, and new ones are being developed every year, especially using semantic
web and LD technologies [9, 5, 7, 2]. We have identified three categories that identify the main
characteristics of interfaces for viewing LD. The first category describes the interaction paradigm
used, the second category describes the type of information to be displayed; the last category
describes the strategies used to reduce the displayed information.
4. By interaction paradigm
Several tools have been developed offering basic interactive operations that allow users to
visually explore KG provided by either a data file or SPARQL endpoint. This subsection lists the
tools for viewing LD classification by interaction paradigm. The interaction paradigm concerns
using tools and processes to produce a visual representation of the data that can be explored
and analyzed directly within the visualization. Different interaction paradigms support different
data-driven insights methodologies. We have identified three types of interaction paradigms:
• Tabular: In interfaces with a tabular interaction paradigm, information about a single
resource is shown in one visualization. Views focus on a table or multiple tables, which
show specific properties linked to the asset, such as media files such as photos, descriptions,
or links to other linked assets.
• Node-link: In the node-link paradigm, resources are represented by nodes or boxes,
while triples are represented by arcs that connect the resources. The node-link view can
be static or dynamic (the latter allowing interaction).
• Visual query composition: The visual query paradigm comprises user interfaces that al-
low the user to perform advanced queries without necessarily having technical knowledge
about the RDF model and the SPARQL language.
4.1. Tabular visualization tools
DBpedia1 (fig. 3) is a project aiming to extract structured content from the information created
in the Wikipedia project2 . DBpedia is presented in tabular format as a list of triples. The resource
page allows users to view the list of all the connections (all the triples in which the resource
acts as a subject/object), including inbound and outbound arcs. The user’s interaction with this
interface provides that all the predicates and objects, when they are resources, can be clicked.
That is, they are links (written in blue and underlined) that allow you to jump from one page to
another, all resources, therefore, have links that allow exploration of related resources.
4.2. Node-Link visualization tools
Aemoo [16] aims to provide additional information than the mere content that can be found in
a SPARQL endpoint, it provides exploratory search over the Web. The tool receives information
from the DBPedia endpoint and improves that content with information gathered over Wikipedia,
Google News3 , and Twitter4 . Its primary focus is to provide information about the resource
building a bridge between the Semantic Web and the traditional Web. Aemoo uses Wikipedia as
its primary source of information. It collects all Wikilinks connected to that resource and divides
them into “set nodes”. Each “set node” contains resources of the same class. Finally, it explores
Google News and Twitter searching for more information connected to the subject. All the set
nodes are displayed on a graph where the core node is the resource of interest. Hovering an
1
Example of DBpedia tabular visualization: https://dbpedia.org/page/England
2
https://www.wikipedia.org
3
https://news.google.com
4
https://twitter.com
Figure 3: DBpedia
entity will show contextual information about the relation between the subject and the hovered
element; generally, that information is a sentence found on Wikipedia. Aemoo approach is
based on knowledge patterns, which represent the core elements contributing to knowledge
about specific events.
LODmilla [21, 22] shows digital library contents with a node-link interaction paradigm
where users can search and extract data associations that are hidden inside the LD with the
help of nodes. LODmilla interface does not display the data that is underlying the documents.
ResearchSpace [53] is an open-source, versatile and flexible platform for working with
digital cultural heritage data in a linked data environment, allowing for better discoverability
and reuse of data. It includes a node-link interaction paradigm with incremental visualization
for knowledge exploration. It also enables a collaborative annotation feature to annotate texts
or images.
4.3. Visual query composition
In a different context, some works study the problem of node-link visual query composition.
These tools allow users without prior knowledge of semantic web technologies to express
SPARQL queries using visual graph representations easily. SPARQLFilterFlow [37] is a web
tool based on a filter/flow model. The tool allows users to formulate SPARQL queries using
graphical elements in a tree-based visualization. Similarly, in QueryVOWL [23, 24] the user uses
visual elements based on VOWL graphical elements to construct graphs that are transformed into
SPARQL queries. This tool’s primary focus is query construction rather than data exploration.
5. By type of information
This subsection lists tools classified by the type of information represented:
• Data Visualization. Tools made to visualize the actual data.
• Model visualization. Tools that show data models (i.e., schemas and ontologies).
• Data to model visualization. Tools that, starting from the data, extract and visualize the
underlying data model.
The tools representing each category are described below.
5.1. Data visualization
Knowledge exploration tools form a particular category of seeking information on many knowl-
edge bases to reveal related information to the searcher and retrieve what users are looking
for. With this search category, the final targets of the search are not known, and the goal itself
is not defined. Therefore, a set of additional activities, for instance, learning, exploration and
evaluation, are accessible through this category. Ariadne [54] is a digital cultural heritage
portal for knowledge exploration in archaeology. It was developed by the European Commission
that provides a centralized access point to digital cultural heritage resources across Europe.
The Ariadne portal is designed to help users discover and explore a wealth of cultural heritage
resources, including collections, digital libraries, archives, and museums. The portal aggregates
metadata from these resources and presents it in a consistent, searchable format, allowing users
to easily find and access digital cultural heritage content from various sources. Additionally,
the Ariadne portal supports linked data principles and provides APIs for integrating the portal
into other applications and services. Overall, the Ariadne portal aims to improve access to
digital cultural heritage resources and facilitate the discovery, reuse, and preservation of cultural
heritage content.
5.2. Model visualization
Ontodia [8] is a Web-based ontology, and semantic data set visualization tool with additional
functionality in sharing and distributing resulting diagrams. Ontodia utilizes the 2D node-link
visualization approach adopting a UML-inspired way of displaying additional information about
the node. In terms of layouts, the tool offers force-directed and grid layouts. It includes the
hierarchical relationships view that displays the parent-child relationships between classes in
a tree layout. Since the tool claims the ability to visualize semantic datasets, it also enables
drag-n-dropping instances on the diagram. The view of the diagram could be freely adjusted by
a user through drag-n-dropping additional items on the canvas, rearranging them, removing
nodes from the graph, and turning links between nodes on and off.
Ontodia supports data exploration capability so the user can sort out the nodes related to
the selected node. From the instance panel, he can drag-n-drop one or several related nodes on
canvas, thus expanding the graph and exploring the ontology. The tool has unique diagram
management features that allow users to publish the fixed URL of the diagram on the Web,
share it with others via the email address or lock it to themselves. The tool introduces the data
source entity, and access to later can be managed similar to controlling access to a diagram.
Searching and filtering are fully available for classes, instances and links. Ontodia was designed
to simplify the visualization of ontologies and semantic data. For this reason, some of the OWL
constructs were omitted, and only the basic ones are kept on the graph.
TopBraid Composer5 , is designed mainly for ontology editing. Its visualization, a side
feature, is not particularly convenient for simple tasks for several reasons. Composer’s full
version is paid, while Ontodia is a free service, which could be a decisive factor for researchers.
Moreover, TopBraid exists in the Eclipse environment, which is not comfortable for non-
programmers, while Ontodia is deliberately designed for non-programming users.
WebVOWL [25] is a web application for the user-oriented visualization of ontologies. It im-
plements the visual notation for OWL ontologies (VOWL) by providing graphical depictions for
elements of OWL that are combined to a force-directed graph layout representing the ontology.
Interaction techniques allow users to explore the ontology and customize the visualization. The
VOWL visualizations are automatically generated from JSON files into which the ontologies
need to be converted. A Java-based OWL2VOWL converter is provided along with WebVOWL.
The force-directed graph layout uses a physics simulation where the forces are iteratively
applied, resulting in an animation that dynamically positions the graph nodes. The energy
of the forces cools down in each iteration, and the layout animation stops automatically after
some time to provide a stable graph visualization. WebVOWL renders the graphical elements
according to the VOWL specification.
5.3. Data to model visualization (schema extraction)
Recently, the problem of data to model visualization, namely the schema extraction, has been
examined in the context of LD. VizLOD [35], LD-VOWL [19], and RDF2Graph [36] use
SPARQL queries to process RDF triples to infer schema information. The tools process the LD to
infer the ontology schema. The tools first identify and present the most representative concepts,
using several methods and assumptions, e.g., consider the classes with a more significant number
of instances as representative. Then, the ontology schema is (progressively) visualized as a
graph, offering several interactive operations.
6. Complexity reduction strategies
Viewing a large number of data objects is a challenging task. Providing an overview can be
extremely difficult, even in small datasets, because handling and visualizing datasets come with
information overloading issues. Consequently, visual scalability is a fundamental requirement
of modern systems, which must effectively support data reduction/abstraction on many data
objects. Two strategies have been identified for reducing the number of information displayed:
• Navigational visualization. The visualization in many user interfaces is focused around
a specific data object, typically a resource. The user can see the “neighborhood” of the
current resource and can navigate to directly related resources. This strategy is often
used in the tabular interaction paradigm.
5
https://www.w3.org/wiki/TopBraid
• Incremental visualization. The paradigm of incremental visualization is often adopted
in dynamic node-link user interfaces. The user controls a workspace where they can
add or remove views of specific data objects from the dataset as needed. There are often
shortcuts to visualize data objects related to the objects already in view.
• Summarized Visualization. In order to offer an overview of a dataset, while avoiding
the problem of overplotting (related to visual information overloading) in large graph
visualizations, several tools use data reduction techniques to provide graph summaries.
The tools representing each category are described below.
6.1. Navigational Visualization
Far from the paradigm of simple linear search results lists, new and more expressive navigation
features, such as node-link views, cluster maps, geographic maps and timelines, support the user
in the perception of information. Due to the high diversity of relationships between entities,
interfaces must be highly generic. This requirement needs methods to structure information
visually based on the user’s interests. Therefore, it is necessary to assign specific relevance to
related entities. For example, there are more than 600 facts (RDF triples) to view information
about the DBpedia entity “Imperatore Augusto”6 . This amount of information cannot be
presented to the user at a glance. Furthermore, each user may have different preferences.
Heuristics based on the statistical and semantic analysis of the underlying RDF graph structure
are applied to classify related entities based on their relevance. Therefore, relevance rankings
need to be customized. User behavior can be monitored by analyzing the log file. With the
user’s preferences, it is possible to generate a profile and map it to a linked open data sub-chart,
representing the user’s interests. This allows subjective relevance ranking and personalized
search recommendations.
6.2. Incremental visualization
Fenfire [33], LodLive [20], and LodView7 are Web exploratory tools that allow users to browse
LD using dynamic node-link interaction paradigm. The user can explore LD by following the
links starting from a given URI or a SPARQL endpoint.
LodLive is a LD visualization tool that allows the user to explore a dataset starting from a
single resource and then to widen the research by expanding the properties of the elements
displayed on the screen. LodLive can retrieve information from several endpoints after providing
the URI of a resource. It also provides an autocompletion feature to the users for choosing the
URI of the resource of interest. Resources are drawn on the screen through circles that contain
the value of the property rdfs:label; then, other smaller circles are drawn concentrically. Every
circle represents an object connected to the central resource.
6
https://dbpedia.org/page/Augustus
7
https://lodview.it
6.3. Summarized Visualization
H-BOLD [18] (fig. 4), the acronym for High-level visualization over Big Linked Open Data, is
the successor of LODeX. It aims to help the general user, without any knowledge about SPARQL
and the content of a dataset, to explore significant sources of linked open data. For summarized
representation and exploration, H-BOLD collects information (number of triples, class list,
property list, and class relations) about datasets in a data store and uses that information for
generating a “Schema Summary”. If the number of classes is high, a community detection
algorithm is executed to shrink the graph into the “Cluster Schema”. Cluster and Schema
summaries can be expanded on-the-fly for detailed visualization of the dataset. The tool can
create a fast and compact overview of the content of a SPARQL endpoint.
Figure 4: H-BOLD
In the same context, RDF4U [26] offers graph visualization over summarized graphs. Re-
searchers noticed that graphs are potent instruments for enhancing human comprehension due
to their readability and synthesis, but this is true when graphs display only relevant information.
The RDF4U visualization approach is based on combining graph simplification, triple ranking,
and property selection. The tool automatically analyzes data collected after querying and
searches for redundant information during graph simplification.
The Graphless [17] visualization tool generates summaries based on statistical data, e.g.,
nodes’ connectivity degree and property frequency.
6.4. Paths discovery
Some tools focus on analyzing the associations between LD entities. In this context, associations
(i.e., relationships) are considered the paths connecting the entities in the LD graph.
Arca [13, 14] (fig. 5) is a tool for the knowledge exploration of digital libraries. It is a modular
tool that includes a knowledge extraction and semantic enrichment engine and an interface
for searching and exploring data. The node-link visualization has been selected as a paradigm
with a multi-level representation of information. The books and their associated contents are
highlighted at the centre of everything. The mode of interaction is incremental concerning
the information sought: we pass from the particular to arrive at the general. Interaction
supports serendipity, a concept according to which unexpected information can be reached
by exploring and navigating the graph. Arca allows the creation of visual queries through the
component “trace path” integrated. Trace path allows the achievement of information common
to two selected concepts, such as books containing two concepts or concepts common to two
books. Furthermore, Arca integrates an association validation system to support a collaborative
improvement of data quality [11].
Figure 5: Arca tool
RelFinder [28] is a Web-based tool that offers interactive discovery and visualization of
relationships between selected LD resources. It includes object mapping, relationship search
(datasets are crawled to find the highest number of relations), visualization (help the user
understand what has been extracted in the previous step) and interactive exploration.
RelClus [31] uses class/property hierarchies to generate hierarchies of paths.
WiSP [30] (Weighted Shortest Paths for RDF Graphs) uses weighted shortest path algorithms
to identify and present the most relevant paths between two resources. The weights are
computed based on several metrics, e.g., PageRank and node degree.
The LODVader [32] (LOD Visualization, Analytics and DiscovERy) system visualizes linked
datasets, following the graph layout used in the LOD cloud visualization, where the nodes
represent the datasets and the edges the links between them.
Yewno Discover [4] (fig. 6) is an integrated system that offers classification and visual
exploration of academic materials to help scholars in their research, but is not adaptable and
flexible to different contexts of use, except with ad hoc adjustments. Furthermore, in respect to
the Arca system, it makes limited use of the KG structure for exploration (node-link interaction
paradigm with static view).
Figure 6: Yewno Discover
Sampo-UI [15] is a framework that provides a set of reusable and extensible components,
application state management, and a read-only API for SPARQL queries, which can be used to
create a user interface for a semantic portal. Sampo uses different search paradigms: free-text
search, faceted search, geospatial search, and temporal search. It provides the users with different
views of the search results in tables, lists, geospatial, or temporal visualizations. Differently
from Sampo UI, the system proposed in this thesis, offer also a knowledge extractor service
from unstructured data and a semantic enrichment service.
7. Conclusion
The survey reveals that LD interfaces use different interaction paradigms, each with its strengths.
At the moment, few interfaces combine the different paradigms, and this can be a good point to
focus on to improve the browsing experience of digital library content. Information reduction
strategies are present in most tools and are crucial for exploring complex KGs. However, while
there are different tools and paradigms for data exploration, it is an open challenge to support
the creation and exploration of data models by domain experts who may not possess a strong
technical background on ontologies and RDF.
Acknowledgments
This work has been partly supported by project SCIBA (Regione Lazio and MIUR - Determi-
nazione n. G07413 del 16/06/2021). Miguel Ceriani acknowledges funding from the Italian
Ministry of University and Research (MIUR), within the European program PON R&I 2014–2020
– Attraction and International Mobility (AIM) – project no. COD.AIM1852414, activity 2, line 1.
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