# Melody: A Platform for Linked Open Data Visualisation and Curated
Storytelling

Giulia Renda<EMAIL_ADDRESS>0000-0003-2990-0021 ,  Marilena Daquino
<EMAIL_ADDRESS>0000-0002-1113-7550 and  Valentina Presutti
<EMAIL_ADDRESS>0000-0002-9380-5160 University of BolognaItaly

###### Abstract.

Data visualisation and storytelling techniques help experts highlight
relations between data and share complex information with a broad audience.
However, existing solutions targeted to Linked Open Data visualisation have
several restrictions and lack the narrative element. In this article we
present MELODY, a web interface for authoring data stories based on Linked
Open Data. MELODY has been designed using a novel methodology that harmonises
existing Ontology Design and User Experience methodologies (eXtreme Design and
Design Thinking), and provides reusable User Interface components to create
and publish web-ready article-alike documents based on data retrievable from
any SPARQL endpoint. We evaluate the software by comparing it with existing
solutions, and we show its potential impact in projects where data
dissemination is crucial.

data visualisation; storytelling; Linked Open Data; design thinking; ontology
design

##  1\. Introduction

Nowadays, Linked Open Data has become increasingly important for research
purposes. In particular, scholars in the Humanities and cultural heritage
institutions leverage the potential of Semantic Web technologies by making
cultural heritage data available as Linked Open Data (LOD) (Hyvönen, 2020).
However, data can often be explored via sophisticated search interfaces only
(Monaco et al., 2022), which can be overwhelming for users who lack the
necessary technical skills. Data visualisation and storytelling techniques
could help experts to share complex information with a broad audience. In the
cultural heritage domain, stories help users to navigate large content (Fernie
et al., 2 09), serve as interpretive frameworks to carry the values and
meaning of a culture (Bruner, 1991), and convey complex information compactly
(Gershon and Page, 2001; Lombardo and Damiano, 2012).

Unfortunately, existing solutions for LOD visualisation are often targeted to
experts and present constraints that prevent users with little knowledge of
Semantic Web and data engineering to interact with data in creative ways.
Several tools focus on schema-level aspects of datasets, and mostly provide
interfaces to show one type of visualisation only. Even when tools offer a
broad range of visualisations, the narrative element is missing (Desimoni and
Po, 2020).

Nonetheless, user-friendly interfaces that allow users to interact with data,
create stories, and communicate results effectively, are desirable, in order
to increase the awareness and usage of quantitative analysis methods in the
humanities and to foster the success of the Open Data business model.

In this paper, we introduce Make mE a Linked Open Data storY (MELODY), an
open-source online platform for querying and presenting charts based on Linked
Open Data, combining data-driven results with manually curated content in an
article-alike narrative, and publishing web-ready data stories. Editing is
supported by What You See Is What You Get (WYSIWYG) interfaces, walk-through,
and examples. Different authentication levels allow users to collect stories,
to publish them online, and export them in several formats (HTML, PDF, JSON).

MELODY was originally designed to facilitate data dissemination of ten pilot
projects dedicated to cultural heritage data. In their project, scholars
collect sources (e.g. texts, audio files), extract information, and populate a
knowledge graph which can be explored via a number of web interfaces,
including both those for lay people and expert enquiries. The methodology used
to design MELODY stems on the diversity of such data, the competency questions
designed in the requirements elicitation phase, and the tasks of the target
audience. Such a methodology is meant to drive the development from ontology
design to web design. To accommodate diverse project requirements and to keep
a tight dependency with competency questions, MELODY is based on standard
Semantic Web technologies and can be reused with any dataset available via
SPARQL endpoint.

The remainder of the paper is the following. In section 2 we review approaches
to design interfaces for interacting with LOD, we identify existing solutions
addressing data visualisation, and highlight issues. In section 3 we present
our hybrid methodology, highlighting its novelty aspects while mapping
interaction patterns to competency questions. In section 4, we introduce
MELODY and its main components. In section 5 we present a competitive analysis
to validate requirements of our software. In section 6 we discuss results,
potential impact, and limitations. We conclude with future works in section 7.

##  2\. Related work

###  2.1. Methodologies for ontology-driven interface design

In the Semantic Web, the structure of a SPARQL query is closely related to the
ontology terms used to organise the data (Charalampidis and Keramopoulos,
2018). Queries and users’ informative needs drive the design of interfaces for
information seeking purposes, and could be mapped to ontology requirements.
Therefore, one would expect User Interfaces and Experience (UI/UX) design
methodologies to be harmonised with ontology design practices.

On the one hand, several ontology design methodologies currently adopt tools
from Human Computer Interaction (HCI). Such methods adopt a bottom-up approach
to requirements elicitation and rely on domain experts when (1) defining the
domain space and vocabulary, (2) outlining motivating scenarios, and (3)
extracting requirements in the form of natural language Competency Questions
(CQs) (Gruninger, 1995; Noy and Hafner, 1997; Suárez-Figueroa et al., 2015;
Cota et al., 2020). The eXtreme Design (XD) methodology (Presutti et al.,
2009; Carriero et al., 2021) prescribes practises for capturing stakeholder
goals, interests, and tasks, and encourage grouping them under high-level
categories, i.e. personas (Junior and Filgueiras, 2005), in turn characterised
by stories, expectations, and priority levels. Stories are powerful tools for
designing experiences as they present facts connected by causal relationships
and help frame the motivating aspects of users or to describe unforeseen
situations (Gruen et al., 2002). So doing, the knowledge acquisition process
borrows concepts and tools closer to UI/UX design practices.

On the other hand, it has been demonstrated that ontology-driven approaches
have brought significant benefit in reducing interface requirements ambiguity
(Dermeval et al., 2016; Amna and Poels, 2022) when supporting software
development (Dermeval et al., 2016; de Souza et al., 2018; Yang et al., 2019),
and requirements formulation, e.g. by creating user stories as structured data
(Thamrongchote and Vatanawood, 2016). Task ontologies and taxonomies for
describing interactive user behaviours and UI elements exist (Ramaprasad and
Syn, 2014; Paulheim and Probst, 2013; Silva et al., 2017), and have been used
to support the assessment of prototypes and final interfaces. Similarly,
ontologies and algorithms addressing HCI design in the design of web
applications have been discussed (Bakaev and Gaedke, 2016; Bakaev and
Avdeenko, 2010). While such efforts focus on the description of aspects of the
HCI discipline, they do not operationalise the descriptive knowledge of domain
ontologies (those used to represent the data and not the UI/UX process) into
prescriptive models, i.e. defining how a system is supposed to behave
according to ontology requirements (e.g. CQs).

Design Thinking (DT) (Rowe, 1991; Brown, 2008; Dorst, 2011; Norman, 2013) is a
well-known design methodology that effectively improves the quality of the
ideas generated and reduces the risk of failure (Liedtka, 2017). It consists
of six phases: empathise, define, ideate, prototype, test, and implement.
Scholars have tried to encapsulate the logic of DT into ontologies to evaluate
design ideas (Ramaprasad and Syn, 2014), to formally describe empathy models
(Pileggi, 2021), or other procedural aspects of HCI (Paulheim and Probst,
2013; Silva et al., 2017; Bakaev and Avdeenko, 2010; Bakaev and Gaedke, 2016).
While such results provide us formal definitions of HCI and DT methods, they
do not inform us on how to leverage domain ontologies in the DT process and
neglect considerations on the overall, prescriptive framework.

To the best of our knowledge, there is no methodology that supports a research
team from the early stages of ontology design to the selection of UI/UX
approaches. Nonetheless, this is often the case in projects where knowledge
graph generation methods are tied to user-friendly interfaces for information
seeking, exploration, and discovery (Hyvönen, 2020), e.g. projects dealing
with the dissemination of cultural heritage on the web (Konstantakis and
Caridakis, 2020).

###  2.2. Linked data visualisation and storytelling

Presentational aspects of Linked Open Data have attracted the interest of
scholarly works in the last decade (Brunetti et al., 2013a; Anutariya and
Dangol, 2018). In recent studies (Desimoni and Po, 2020) 77 data visualisation
tools, including those with a focus on Linked Data, have been reviewed. The
survey demonstrates that there is a lack of adequate solutions for LOD-based
storytelling (YourDataStories111https://platform.yourdatastories.eu/ (Petasis
et al., 2018) is the only non-working example), most solutions are not
available or focus on schema-level analysis only.

Several problems affect existing solutions, spanning from data access and
export, to limitations in charting and storytelling strategies. As highlighted
in (Bikakis and Sellis, 2016), most exploration and visualisation systems work
offline (e.g. GraphVizdb (Bikakis et al., 2016)) or are limited to accessing
pre-processed, small, datasets (e.g. VisGraph`^`3
222https://visgraph3.github.io/, RDFShape (Gayo et al., 2018)). Moreover,
access to datasets may be limited to those that are not accessible via SPARQL
endpoints or API (e.g. Graphless (Santana-Pérez, 2018), Rhizomer (Brunetti et
al., 2013b)). Therefore, developing interfaces for the live exploration of
large datasets is problematic. When several sources can be explored, tools
focus on schema-level analysis (e.g. H-BOLD (Po and Malvezzi, 2018), WebVOWL
(Lohmann et al., 2015)) or present RDF data in one format only, often the
tabular form (LodView 333https://lodview.it/, RDFSurveyor (Vega-Gorgojo et
al., 2019)). Tools that do not face the above issues, still, do not offer the
user the possibility to present data in an author-controlled narrative and
allow for image download only (e.g. RAWGraphs 444https://www.rawgraphs.io/,
SynopsViz (Bikakis et al., 2017)). Nonetheless, building a narrative,
presenting the data in the right order, and limiting results to selected
visualisations that convey the information as simply and concisely as
possible, are common methods used in storytelling techniques to bridge users’
cognitive gap (Stackpole, 2020). Moreover, the What-You-See-Is-What-You-Get
(WYSIWYG) paradigm is deemed a powerful way to give the author control while
creating such narratives (Khalili et al., 2012).

##  3\. Methodology and requirements collection

The diversity of data and audiences of projects dedicated to cultural heritage
requires a sound methodological approach to drive the development from
knowledge acquisition to web design. We introduce a modular workflow
harmonising eXtreme Design and Design Thinking methodologies. The proposed
methodology spans from data and user requirements collection to the
development of prototypes and their evaluation. In particular, the empathise
phase of Design Thinking is integrated with methods proposed by eXtreme
Design, such as the extraction of competency questions (CQs) from user
stories, and the mapping of CQs to ontology design patterns. Secondly, a
mapping between CQs and data patterns allows us to gather UI/UX requirements
targeted to scholars. Data and interaction patterns guided the design and
prototyping of UI components of MELODY, and highlighted alternative strategies
for User Experience design.

###  3.1. From Ontology Design to Storytelling Interfaces

In Fig. 1 we illustrate the phases of our proposed methodology, grouped under
the phases of Design Thinking. We highlight methods borrowed from the two
methodologies (grey blocks) and our novel contributions (white blocks).

Figure 1. Methodology overview.

A diagram that showcases the phases of the workflow described in 3.1.

In detail, interviews with stakeholders are performed to define (1) users’
motivational aspects, (2) a list of competitors of the product to be
developed, (3) user behaviours (journeys), (4) limitations and benefits of
competitors, (5) expectations in terms of content and application
requirements. Resources mentioned during interviews are included in a
competitive analysis, which addresses a classification of aspects relevant to
knowledge organisation, User Interface design, and visual identity. A narrow
list of reference solutions is further investigated via user studies.

The ontology design team outlines personas, i.e. stereotypical users presented
in the form of textual descriptions. Situations, tasks, interests, and
expectations, are grouped under one or more user stories for each persona.
Competency questions are extracted from stories via content analysis. CQs
summarise salient aspects of the story in the form of natural language
questions, address ontology and data requirements, and semantic constraints on
properties and classes. We propose to further annotate CQs with (1) ontology
patterns, including classes and properties of entities involved, (2) a
classification of the scope of data (e.g. historical, musical, bibliographical
data), (3) whether the CQ addresses a task (e.g. search, share) or secondary
detail, and (4) the type of expected result, e.g. strings, lists, tables, or
charts.

Stories and CQs are analysed to grasp user journeys, i.e. the flow of
searches, reading behaviours, and emotional states. We propose two alternative
ways to design journeys from content requirements. If collected personas are
rather homogeneous and in a small amount, driver CQs can be identified, and
other CQs can be clustered. Driver CQs are those that best summarise the scope
of data and the task (e.g. search for artefacts grouped under categories).
Other CQs focused on contextual aspects can be grouped (e.g. in info boxes).
In case personas, stories, and CQs are too many to be singularly addressed,
Exploratory Data Analysis (EDA) can be performed over annotated CQs.

Brainstorming sessions help to make sense of prior results, leading the
preparation of mockups. To support this activity, we propose to integrate two
analyses: a task analysis, where CQs are aggregated by task, sorted by
complexity of the task, and separated accordingly into different interfaces; a
thematic analysis, based on the distribution of classes and their position in
data patterns (i.e. as input or output), in order to understand common access
points to data.

Focus groups with stakeholders involved in the definition of requirements
provide meaningful feedback, helping to reframe requirements that were unclear
or incorrect. Usability tests and heuristic evaluation with 3-5 people groups
are considered sufficient to discover up to 90% of issues (Nielsen, 1994). To
include secondary user targets in the evaluation, we propose a user study
focused on co-design. While the initial user study (Customer feedback) guided
participants in the evaluation of existing solutions, in a co-design user
study, participants have to imagine themselves in a comfortable scenario (e.g.
”you are at home studying”), are provided with a task (e.g. ”you want to
discover new music”), and must share insights on how they would achieve their
goal. Results of the survey contribute to a preliminary evaluation of the
mockups (despite these are not given to participants). If results are
satisfying, mockups are implemented, tested, and solutions are deployed in
production.

###  3.2. Requirements elicitation

To design MELODY, we relied on 220 CQs555https://github.com/polifonia-
project/stories extracted from stories of historians, musicologists, and
heritage professionals involved in the development of ten pilot datasets, and
that later on became early adopters of MELODY. In detail, CQs belong to 19
personas, and were extracted from 28 stories. We annotated the CQs with
entities, relations, and potential UI/UX search patterns. Via aggregated data
analysis, we analysed the distribution of CQs in terms of entities and
properties, and we estimated common data patterns along with recurring
interaction patterns (e.g., CQs returning lists, charts, maps). Stories
relevant to data visualisation tasks are individually analysed, and driver CQs
are identified. We assigned a main UI/UX pattern to each story, including
expected visualisations, content types, and
tasks666https://github.com/polifonia-project/web_portal/tree/main/analysis. To
date, the analysis has focused on three main interaction patterns, namely:

  * •

an article-like interface where curated text can be accompanied by charts
(e.g. bar charts, pie charts, line charts);

  * •

an interactive text search, where the results of a search can feed new
searches via reusable queries/components that return new charts (e.g. tables);

  * •

a geographic map, including aids to filter out data points, visualise metadata
and statistical information.

While such interaction patterns could be used as different, standalone,
interfaces, MELODY makes use of all of them into a single template wherein UI
components can be combined in a unique narrative.

Functional requirements of the application are also gathered via the
competitive analysis, where tools mentioned in section 2 and those proposed by
stakeholders are analysed according to problems emerged in the literature
review. Results of the competitive analysis also guided the evaluation of
MELODY.

##  4\. MELODY: an overview

Make mE a Linked Open Data storY, MELODY is a web-based, user-friendly
platform for (1) querying Linked Open Data available from any SPARQL endpoint,
(2) presenting query results as charts, optionally along with curated content,
and (3) publishing results as web-ready, exportable, data stories. To create a
data story, users need (1) the URL of a SPARQL endpoint and (2) some prior
knowledge of SPARQL query language. In fact, any chart and interactive
component is derived from an input SPARQL query (written by the user) against
the selected endpoint.

In this section we introduce technologies, authentication and authoring
mechanisms, and data story components.

MELODY is a light-weight application based on
Flask777https://flask.palletsprojects.com/ and React888https://react.dev/
(non-native) frameworks, that respectively manage backend APIs and templating,
and UI components. Any Linked Open Data source available via a public SPARQL
endpoint can be queried (currently client-side) and used to build a data
story. MELODY can be installed locally or accessed via web browser. The source
code, the documentation, and a demo are available online999Source code:
https://github.com/polifonia-project/dashboard; documentation:
https://polifonia-project.github.io/dashboard/..

Users can create stories anonymously or via a GitHub authentication (Fig. 2),
with the only difference being the publication venue of their stories. Any
online instance of MELODY must be paired to a GitHub organisation, which
includes members of the project maintaining the application, who in turn have
access to a self-standing application for authoring and publishing. Data
stories created by members of the GitHub organisation are published on the
website of MELODY, and are publicly available from the left sidebar. GitHub
users that are not organisation members can access any online MELODY instance
and use all MELODY features, including the final publication. However, their
stories are published as static web documents on a separate catalogue, i.e. a
GitHub repository with GitHub pages enabled, that is hosted by the GitHub
organisation maintaining MELODY. Indeed, organisation members maintaining the
web application may decide to make available their authoring interface to
external users, although moving anonymous stories from their instance to a
separate, low-maintenance, instance of GitHub pages. For instance, the online
demo of MELODY101010https://projects.dharc.unibo.it/melody/ is paired with an
external catalogue, currently used for testing purposes111111https://melody-
data.github.io/stories/. Finally, anonymous users have access to all the
features but the publication of their web-ready data stories, which can only
be downloaded at the end of their session.

Figure 2. Homepage with authentication (1), sidebar with published data
stories (2) and modify button (3).

A screenshot of the Melody homepage that shows the sidebar to access published
data stories, the button to modify them, and the login/logout button.

As aforementioned, the main requirement to create a data story is the URL of a
SPARQL endpoint, and some information to set up the canvas of the data story
(Fig. 3). Currently, the statistics template includes all the UI components
available in MELODY. If the user is a member of the hosting organisation, they
can choose or create a section title, which appears in the left sidebar of the
website and groups stories under a common theme. An initial title must be
provided to initialise the canvas.

Figure 3. Setup form to start creating a data story.

A screenshot that shows a form to be filled in with information about type of
template, section in which to include the story, the title and the SPARQL
endpoint.

Figure 4. The initial WYSIWYG interface with the list of components.

A screenshot that shows a form to be filled in with information about title,
subtitle, description about a story and buttons to add components (text,
numbers, charts, table, map, textsearch with actions.

The user is redirected to the data story canvas, where they can choose among
several components, pick the colour palette of the interactive elements, and
start authoring the story by selecting components to be added (Fig. 4).
Content creation is simplified by a number of WYSIWYG forms, one for each UI
component, that authors can fill in with plain text or SPARQL queries, and
instantly see the preview of results (Fig. 5). Components respond to
requirements extracted during the initial requirements collection. Currently,
available components are the following:

  * •

text, displaying a rich text editor for creating curated texts, like
paragraphs and titles, as HTML content;

  * •

counter, displaying a card with a number and a label;

  * •

chart, currently proposing four chart types, i.e. barchart, line chart,
scatterplot and doughnut chart;

  * •

map, displaying geolocated data on a map, optionally accompanied by a sidebar
with filters and a secondary sidebar to display metadata of data points;

  * •

table, which can include simple text, audio files, or embedded videos;

  * •

text search, displaying a search box for free-text search on a dataset and
returning results in a table;

  * •

action, a query that can be performed on any result of a text search (i.e. a
cell value of the returned table) and that displays new results in another
table. Actions can be attached also to results of another action, and can be
reused on demand with any value.

All components (except the text editor) are accompanied by info boxes that
explain fields, naming conventions to be used (if applicable), e.g. variable
names, data types of results of a SPARQL query, and examples of usage.

Figure 5. An overview of the components: a chart preview (1), the form for the
chart (2), a table preview with audio files (3), a count preview (4), an
example of textsearch (5), the preview of the map with filters (6).

The image is a collage of all the components in Melody. At the top left a
barchart, at the top right a form with type of chart, title and SPARQL query,
in the middle left a table with link and audio file entries, in the middle
right a form with number, label and SPARQL query, at the bottom left a text
search area and a table with link entries, at the bottom right a map centerd
in Liguria with data point and a left sidebar that filters by province.

The resulting data story is a web document that contains an ordered list of UI
components, which can be modified and sorted at any point by authors. All
users can export a story as a web-ready static document (HTML), a PDF
document, or a JSON file (see Fig. 6), which keeps track of the order of
components in the data story, the SPARQL queries designed by the user, and the
type of expected result (e.g. chart, table, HTML text). Likewise, each
component of the datastory can be embedded in other web pages, exported as an
image or CSV file, and the underlying SPARQL query can be seen on demand.

Figure 6. Buttons to export data stories, configuration files, and single
components.

A screenshot that shows a barchart of bells per province, and buttons to
export both the chart and the data story as HTML, PDF, JSON, image or
embeddable code.

##  5\. Evaluation

To validate the soundness of requirements considered to design MELODY, we
performed a comparative analysis with competing solutions and collected
feedback during focus groups with early adopters.

We selected tools from recent surveys (Desimoni and Po, 2020) and we pruned
the list by selecting peer-reviewed solutions that (1) support LOD-native
interrogation (i.e. SPARQL queries over RDF data), (2) provide at least one
charting option as output, (3) are accessible online, (4) are maintained and
working, and (5) are openly available for reuse. We ended up with a short list
of 9 solutions (Table 1), that we analysed according to the following aspects:

  * •

SPARQL: data can be queried from a SPARQL endpoint;

  * •

RDF: data can be queried from a RDF dump;

  * •

Multi (datasets): the tool can be reused with any LOD source;

  * •

Web: the software provides web-based access;

  * •

HTML, Image, embed, CSV, RDF, and PDF: charts or stories can be exported in
multiple formats;

  * •

Pub: the tool provides a publishing venue of outputs;

  * •

Multi (charts): multiple charting solutions are available;

  * •

Intrc: interactive charts or query patterns are available (e.g. clickable
charts, events triggered by click);

  * •

Lay: default pagination of content is provided;

  * •

Cur: curated text can be added along with multiple UI components;

  * •

WYSIWYG: facilitation in editing;

Table 1. A summary of the results of the analysis

Tool | Data Access | Export | Charting | Storytelling
---|---|---|---|---
| SPARQL | RDF | multi | Web | HTML | Img | embed | CSV | RDF | PDF | Pub | multi | Intrc | lay | cur | WYSIWYG
Graphless |  |  |  | x |  | x |  |  |  |  |  |  | x |  |  |
LDVizWiz | x |  |  | x |  |  |  |  | x |  |  | x | x | x |  |
LodView | x |  | x | x |  |  |  |  |  |  |  |  |  | x |  |
RAWGraphs | x | x | x | x |  | x |  |  |  |  |  | x |  |  |  | x
RDFShape | x | x | x | x |  | x | x | x |  |  |  |  |  |  |  |
RDFSurveyor | x |  | x | x |  |  |  |  |  |  |  |  |  | x |  |
Rhizomer | x |  | x | x |  |  |  |  |  |  | x | x |  | x |  |
Sparklis | x |  | x | x |  |  |  | x |  |  |  |  |  |  |  |
WebVOWL |  | x | x | x | x | x |  |  | x |  |  |  | x |  |  |
MELODY | x |  | x | x | x | x | x | x |  | x | x | x | x | x | x | x

In summary, most of the surveyed tools provide a web interface to access
multiple data sources via their SPARQL endpoint. Fewer solutions also allow
access to RDF dumps, usually as an additional option. Outputs can usually be
exported as images or CSV, in a few cases as RDF data. Little support is
offered when downloading results in other formats, such as HTML, embeddable
components, or PDF documents. No solution is designed to host or publish
results on the web, which is delegated to the user and external venues
(LodView is designed as a web application, which can serve data, but it
requires the user to install and maintain both the application and results).
Moreover, only a third of tools offer more than one output (interactive)
chart. 4 tools out of 9 allow the automatic pagination of content in a canvas,
though the control is entirely on the interface side. Notably, none of the
surveyed solutions allow curated content (other than titles and legend) to be
authored and displayed along with the charts, and only one solution
(RAWGraphs) facilitates chart tuning via WYSIWYG interfaces.

##  6\. Discussion

The comparative analysis shows that MELODY is a competitive solution for
Linked Open Data visualisation and storytelling. Although MELODY was initially
developed to support requirements of pilot datasets that are all part of the
cultural heritage domain, the platform can be used with any LOD source
available via a SPARQL endpoint, and provides generic interaction patterns to
accommodate needs of diverse projects. It is flexible to several case studies,
since it facilitates authoring with user-friendly interfaces, allows curated
content to be included in an article-alike web document, offers several
options for charting and exporting outputs, and acts as a venue for web
publication.

It is worth noting that storytelling and authoring features implemented in
MELODY can lower the learning curve that is usually required by visualisation
tools, since it only requires prior knowledge of SPARQL query language.
Despite the reputation of SPARQL of being a language for tech-savvy people
only, recent studies on knowledge transfer in interdisciplinary projects
demonstrate that it is by far the most suitable solution to empower humanists
that want to face complex, data-driven, enquiries, which are rarely addressed
by general purpose portals (Burrows et al., 2022). While MELODY is not
targeted to lay users, it can support beginner-intermediate scholars learning
Semantic Web technologies.

The potential impact of MELODY is also demonstrated by the possibility to
access for free any online instance of the tool that makes available a
publication venue for external users via GitHub pages. So doing, authors can
rely on existing, low cost publishing solutions. Moreover, results are made
available as static HTML pages with no technology dependency, which makes it
easier to move, modify, and maintain the code for users with basic web
development skills (i.e. HTML and CSS skills). Such peculiarities could be
leveraged in scholarly article authoring environments, although some
compelling requirements are currently missing (e.g. citability and
dereferencing of new identifiers).

A few limitations affect the current work. Compared to surveyed tools, neither
access nor export of rdf static files is currently possible. To date, only
SPARQL queries to one endpoint at time can be performed. Federated queries can
be leveraged to perform some reconciliation and graph construction on the fly.
However, queries are performed client-side and query results are not stored.
Therefore, the application may face performance issues when querying large
datasets. Moreover, live queries imply modifiability of results, therefore
hampering the citability of the data story itself. Lastly, focus groups with
early adopters of MELODY provided feedback for validating the online instance,
which currently includes the three aforementioned interaction patterns
(article-alike, geographic exploration and interactive text search). So far,
around ten data stories have been produced by members of pilot projects, and
feedback has been collected in GitHub issues to guide the development and
enhancement. Follow-up workshops with new, external, participants are planned
to estimate the easiness and flexibility of the tool within groups with
different technological skills (e.g. graduated students in Digital
Humanities).

##  7\. Conclusions

In this article we presented MELODY, a platform for Linked Open Data
storytelling. The design of the application was guided by a novel, hybrid,
ontology-driven methodology which leverages requirements expressed in the form
of competency questions. The set of competency questions selected is relevant
to ten pilot projects in the cultural heritage domain, ensuring a degree of
flexibility of realised components. Compared to existing software solutions,
MELODY outperforms in most of the functional requirements we collected from
the literature and from interviews with stakeholders. In future works we will
address current limitations related to data access performance, citability of
data stories, and we will extend the methodology with new pilot projects to
collect new requirements and enrich the platform with new interaction
patterns.

###### Acknowledgements.

This work has received funding from the European Union’s Horizon 2020 research
and innovation programme (Polifonia, G.A. 101004746).

## References

  * (1)
  * Amna and Poels (2022) Anis R Amna and Geert Poels. 2022.  Ambiguity in user stories: A systematic literature review.  145 (2022), 106824\.  Publisher: Elsevier.
  * Anutariya and Dangol (2018) Chutiporn Anutariya and Reshma Dangol. 2018.  VizLOD: Schema Extraction And Visualization Of Linked Open Data. In _2018 15th International Joint Conference on Computer Science and Software Engineering (JCSSE)_. 1–6.  https://doi.org/10.1109/JCSSE.2018.8457325
  * Bakaev and Avdeenko (2010) Maxim Bakaev and Tatiana Avdeenko. 2010.  Ontology to support web design activities in e-commerce software development process. In _Proceedings of the IASTED International Conference on Automation, Control, and Information Technology-Information and Communication Technology, ACIT-ICT_. 241–248.
  * Bakaev and Gaedke (2016) Maxim Bakaev and Martin Gaedke. 2016.  Application of evolutionary algorithms in interaction design: from requirements and ontology to optimized web interface. In _2016 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)_. IEEE, 129–134.
  * Bikakis et al. (2016) Nikos Bikakis, John Liagouris, Maria Krommyda, George Papastefanatos, and Timos Sellis. 2016\.  graphVizdb: A scalable platform for interactive large graph visualization. In _2016 IEEE 32nd International Conference on Data Engineering (ICDE)_. 1342–1345.  https://doi.org/10.1109/ICDE.2016.7498340
  * Bikakis and Sellis (2016) Nikos Bikakis and Timos Sellis. 2016.  Exploration and Visualization in the Web of Big Linked Data: A Survey of the State of the Art.  (2016).  arXiv:1601.08059 http://arxiv.org/abs/1601.08059
  * Bikakis et al. (2017) Nikos Bikakis, Melina Skourla, and George Papastefanatos. 2017\.  rdf:SynopsViz - A Framework for Hierarchical Linked Data Visual Exploration and Analysis.  https://doi.org/10.48550/arXiv.1408.3148 arXiv:1408.3148 [cs]
  * Brown (2008) Tim Brown. 2008\.  Design thinking.  86, 6 (2008), 84\.
  * Bruner (1991) Jerome Bruner. 1991\.  The Narrative Construction of Reality.  18, 1 (1991), 1–21.  https://www.jstor.org/stable/1343711 Publisher: The University of Chicago Press.
  * Brunetti et al. (2013a) Josep Maria Brunetti, Sören Auer, Roberto García, Jakub Klímek, and Martin Nečaský. 2013a.  Formal Linked Data Visualization Model. In _Proceedings of International Conference on Information Integration and Web-based Applications & Services_ (New York, NY, USA) _(IIWAS ’13)_. Association for Computing Machinery, 309–318.  https://doi.org/10.1145/2539150.2539162
  * Brunetti et al. (2013b) Josep Maria Brunetti, Roberto García, and Sören Auer. 2013b.  From Overview to Facets and Pivoting for Interactive Exploration of Semantic Web Data.  9, 1 (2013), 1–20.  https://doi.org/10.4018/jswis.2013010101 Publisher: IGI Global.
  * Burrows et al. (2022) Toby Burrows, Laura Cleaver, Doug Emery, Eero Hyvönen, Mikko Koho, Lynn Ransom, Emma Thomson, and Hanno Wijsman. 2022\.  Medieval manuscripts and their migrations: Using SPARQL to investigate the research potential of an aggregated Knowledge Graph.  _Digital Medievalist_ (2022).
  * Carriero et al. (2021) Valentina Anita Carriero, Aldo Gangemi, Maria Letizia Mancinelli, Andrea Giovanni Nuzzolese, Valentina Presutti, and Chiara Veninata. 2021\.  Pattern-based design applied to cultural heritage knowledge graphs.  12, 2 (2021), 313–357.  Publisher: IOS Press.
  * Charalampidis and Keramopoulos (2018) Charalampos C Charalampidis and Euclid A Keramopoulos. 2018.  Semantic Web user interfaces–A model and a review.  _Data & Knowledge Engineering_ 115 (2018), 214–227.
  * Cota et al. (2020) Giuseppe Cota, Marlena Daquino, and Gian Luca Pozzato. 2020\.  _Applications and Practices in Ontology Design, Extraction, and Reasoning_. Vol. 49.  IOS Press.
  * de Souza et al. (2018) Pedro Lopes de Souza, Antonio Francisco do Prado, Wanderley Lopes de Souza, Sissi Marilia dos Santos Forghieri Pereira, and Luís Ferreira Pires. 2018\.  Improving agile software development with domain ontologies. In _Information Technology-New Generations: 15th International Conference on Information Technology_. Springer, 267–274.
  * Dermeval et al. (2016) Diego Dermeval, Jéssyka Vilela, Ig Ibert Bittencourt, Jaelson Castro, Seiji Isotani, Patrick Brito, and Alan Silva. 2016.  Applications of ontologies in requirements engineering: a systematic review of the literature.  21 (2016), 405–437.  Publisher: Springer.
  * Desimoni and Po (2020) Federico Desimoni and Laura Po. 2020.  Empirical evaluation of Linked Data visualization tools.  112 (2020).  https://doi.org/10.1016/j.future.2020.05.038
  * Dorst (2011) Kees Dorst. 2011\.  The core of ‘design thinking’and its application.  32, 6 (2011), 521–532.  Publisher: Elsevier.
  * Fernie et al. (2 09) Kate Fernie, Jillian Griffiths, Mark Stevenson, Paul Clough, Paula Goodale, Mark Hall, Phil Archer, Konstantinos Chandrinos, Eneko Agirre, Oier Lopez de Lacalle, Andrea de Polo, and Runar Bergheim. 2012-09.  PATHS: Personalising access to cultural heritage spaces. In _2012 18th International Conference on Virtual Systems and Multimedia_. 469–474.  https://doi.org/10.1109/VSMM.2012.6365960
  * Gayo et al. (2018) Jose Emilio Labra Gayo, Daniel Fernández-Álvarez, and Herminio García-González. 2018.  RDFShape: An RDF Playground Based on Shapes. In _SEMWEB_.
  * Gershon and Page (2001) Nahum Gershon and Ward Page. 2001.  What storytelling can do for information visualization.  44, 8 (2001), 31–37.  https://doi.org/10.1145/381641.381653
  * Gruen et al. (2002) Dan Gruen, Thyra Rauch, Sarah Redpath, and Stefan Ruettinger. 2002\.  The use of stories in user experience design.  14, 3 (2002), 503–534.  Publisher: Taylor & Francis.
  * Gruninger (1995) Michael Gruninger. 1995\.  Methodology for the design and evaluation of ontologies. In _Proc. IJCAI’95, Workshop on Basic Ontological Issues in Knowledge Sharing_.
  * Hyvönen (2020) Eero Hyvönen. 2020\.  Using the Semantic Web in digital humanities: Shift from data publishing to data-analysis and serendipitous knowledge discovery.  11, 1 (2020), 187–193.  Publisher: IOS Press.
  * Junior and Filgueiras (2005) Plinio Thomaz Aquino Junior and Lucia Vilela Leite Filgueiras. 2005\.  User modeling with personas. In _Proceedings of the 2005 Latin American conference on Human-computer interaction_. 277–282.
  * Khalili et al. (2012) Ali Khalili, Sören Auer, and Daniel Hladky. 2012.  The RDFa Content Editor - From WYSIWYG to WYSIWYM. In _2012 IEEE 36th Annual Computer Software and Applications Conference_. 531–540.  https://doi.org/10.1109/COMPSAC.2012.72 ISSN: 0730-3157.
  * Konstantakis and Caridakis (2020) Markos Konstantakis and George Caridakis. 2020.  Adding culture to UX: UX research methodologies and applications in cultural heritage.  13, 1 (2020), 1–17.  Publisher: ACM New York, NY, USA.
  * Liedtka (2017) Jeanne Liedtka. 2017\.  Evaluating the impact of design thinking in action. In _Academy of Management Proceedings_ , Vol. 2017. Academy of Management Briarcliff Manor, NY 10510, 10264.  Issue: 1.
  * Lohmann et al. (2015) Steffen Lohmann, Vincent Link, Eduard Marbach, and Stefan Negru. 2015.  WebVOWL: Web-based Visualization of Ontologies. In _Knowledge Engineering and Knowledge Management_ (Cham), Patrick Lambrix, Eero Hyvönen, Eva Blomqvist, Valentina Presutti, Guilin Qi, Uli Sattler, Ying Ding, and Chiara Ghidini (Eds.). Springer International Publishing, 154–158.  https://doi.org/10.1007/978-3-319-17966-7_21
  * Lombardo and Damiano (2012) Vincenzo Lombardo and Rossana Damiano. 2012.  Storytelling on mobile devices for cultural heritage.  18, 1 (2012).  https://doi.org/10.1080/13614568.2012.617846 Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/13614568.2012.617846.
  * Monaco et al. (2022) Daniele Monaco, Maria Angela Pellegrino, Vittorio Scarano, and Luca Vicidomini. 2022\.  Linked open data in authoring virtual exhibitions.  53 (2022), 127–142.  https://doi.org/10.1016/j.culher.2021.11.002
  * Nielsen (1994) Jakob Nielsen. 1994\.  Guerrilla HCI: Using discount usability engineering to penetrate the intimidation barrier.  _Cost-justifying usability_ (1994), 245–272.
  * Norman (2013) Don Norman. 2013\.  _The design of everyday things: Revised and expanded edition_.  Basic books.
  * Noy and Hafner (1997) Natalya Fridman Noy and Carole D Hafner. 1997.  The state of the art in ontology design: A survey and comparative review.  18, 3 (1997), 53–53.
  * Paulheim and Probst (2013) Heiko Paulheim and Florian Probst. 2013.  UI 2 Ont—A Formal Ontology on User Interfaces and Interactions.  (2013), 1–24.  Publisher: Springer.
  * Petasis et al. (2018) Georgios Petasis, Anna Triantafillou, and Eric Karstens. 2018\.  YourDataStories: Transparency and corruption fighting through data interlinking and visual exploration. In _Internet Science: INSCI 2017 International Workshops, IFIN, DATA ECONOMY, DSI, and CONVERSATIONS, Thessaloniki, Greece, November 22, 2017, Revised Selected Papers 4_. Springer, 95–108.
  * Pileggi (2021) Salvatore F Pileggi. 2021\.  Knowledge interoperability and re-use in Empathy Mapping: An ontological approach.  180 (2021), 115065\.  Publisher: Elsevier.
  * Po and Malvezzi (2018) L. Po and D. Malvezzi. 2018\.  High-level visualization over big linked data, Vol. 2180.  ISSN: 1613-0073.
  * Presutti et al. (2009) Valentina Presutti, Enrico Daga, Aldo Gangemi, and Eva Blomqvist. 2009.  eXtreme design with content ontology design patterns. In _Proc. Workshop on Ontology Patterns_. 83–97.
  * Ramaprasad and Syn (2014) Arkalgud Ramaprasad and Thant Syn. 2014.  Design thinking and evaluation using an ontology. In _Design Science: Perspectives from Europe: European Design Science Symposium, EDSS 2013, Dublin, Ireland, November 21-22, 2013. Revised Selected Papers_. Springer, 63–74.
  * Rowe (1991) Peter G Rowe. 1991\.  _Design thinking_.  MIT press.
  * Santana-Pérez (2018) Idafen Santana-Pérez. 2018\.  Graphless: Using Statistical Analysis and Heuristics for Visualizing Large Datasets. In _VOILA@ISWC_.
  * Silva et al. (2017) Thiago Rocha Silva, Jean-Luc Hak, and Marco Winckler. 2017\.  A formal ontology for describing interactive behaviors and supporting automated testing on user interfaces.  11, 4 (2017), 513–539.  Publisher: World Scientific.
  * Stackpole (2020) Beth Stackpole. 2020\.  _The next chapter in analytics: data storytelling_.  https://mitsloan.mit.edu/ideas-made-to-matter/next-chapter-analytics-data-storytelling
  * Suárez-Figueroa et al. (2015) Mari Carmen Suárez-Figueroa, Asunción Gómez-Pérez, and Mariano Fernandez-Lopez. 2015.  The NeOn Methodology framework: A scenario-based methodology for ontology development.  10, 2 (2015), 107–145.  Publisher: IOS Press.
  * Thamrongchote and Vatanawood (2016) Chaleerat Thamrongchote and Wiwat Vatanawood. 2016.  Business process ontology for defining user story. In _2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS)_. IEEE, 1–4.
  * Vega-Gorgojo et al. (2019) Guillermo Vega-Gorgojo, Laura Slaughter, Bjørn Marius Von Zernichow, Nikolay Nikolov, and Dumitru Roman. 2019.  Linked Data Exploration With RDF Surveyor.  7 (2019), 172199–172213.  https://doi.org/10.1109/ACCESS.2019.2956345 Conference Name: IEEE Access.
  * Yang et al. (2019) Lan Yang, Kathryn Cormican, and Ming Yu. 2019.  Ontology-based systems engineering: A state-of-the-art review.  111 (2019), 148–171.  Publisher: Elsevier.