=Paper=
{{Paper
|id=Vol-3667/DS-LAK24_paper_1
|storemode=property
|title=Data Storytelling on Multi-Modal Knowledge Graph via Data Comics: a Case Study in Yanyuwa Language
|pdfUrl=https://ceur-ws.org/Vol-3667/DS-LAK24_paper_1.pdf
|volume=Vol-3667
|authors=Zhiping Liang,Zijie Zeng,Gloria Fernandez Nieto,Yuheng Li,Yi-Shan Tsai,Guanliang Chen,Zachari Swiecki,Dragan Gašević,John Bradley,Lele Sha
|dblpUrl=https://dblp.org/rec/conf/lak/LiangZFLTCSGBS24
}}
==Data Storytelling on Multi-Modal Knowledge Graph via Data Comics: a Case Study in Yanyuwa Language==
https://ceur-ws.org/Vol-3667/DS-LAK24_paper_1.pdf
Data Storytelling on Multi-modal Knowledge Graph
via Data Comics: a case study in Yanyuwa Language
Zhiping Liang1 , Zijie Zeng1 , Gloria Fernandez Nieto1 , Yuheng Li1 , Yi-Shan Tsai1 ,
Guanliang Chen1 , Zachari Swiecki1 , Dragan Gašević1 , John Bradley2 and Lele Sha1,∗
1
Center for Learning Analytics at Monash (CoLAM), Monash university
2
Monash Indigenous Studies Centre, Monash university
Abstract
This paper pioneers a study of storytelling on a multi-modal and multi-grained knowledge graph of
the Yanyuwa language - a critically endangered Indigenous Australian language. Unlike traditional
data management technologies (e.g., relational database), the knowledge graph is capable of connecting
individual unit of entities (e.g., people, location or an abstract concept) to their cultural significance (e.g.,
oral traditions, historical context, examples of the concept), which is crucial for capturing the cultural and
historical significance embedded in languages to support various stakeholders (e.g., indigenous educators)
to revive the language. To ensure that the knowledge encoded can be accessible and understandable to
non-technical stakeholders, who typically have no prior knowledge about the knowledge graph, this
paper investigate a data comic approach to transform complex graphical knowledge into narratives
that are easier to understand and more engaging for the non-technical audience. We report on: (i)
technical details involved in constructing the knowledge graph; (ii) processes involved in transforming a
graph segment into a narrated knowledge stories; and (iii) the design and prototyping process of the
eventual data comic visualisations carried out by the three knowledge graph engineers. In doing so, we
contributes novel insights regarding the role of data storytelling under the circumstances of employing a
technically advanced solution like knowledge graph in the longstanding challenge of reviving endangered
indigenous languages.
Keywords
Data storytelling, Multi-modal knowledge graph, Data comics
1. Introduction
“Nakari ridinja nungka rikarrangu nya-mangaji ni-maliji, kilha-nyngkarri nyinku
wuka ngala yinda wukanyinjawu, yinbayawu ji-awarawu, barra bawuji wakara
Joint Proceedings of LAK 2024 Workshops, co-located with the 14th International Conference on Learning Analytics and
Knowledge (LAK 2024), Kyoto, Japan, March 18-22, 2024.
∗
Corresponding author.
Envelope-Open zhiping.liang@monash.edu (Z. Liang); Zijie.Zeng@monash.edu (Z. Zeng);
GloriaMilena.FernandezNieto@monash.edu (G. F. Nieto); yuheng.li@monash.edu (Y. Li); Yi-Shan.Tsai@monash.edu
(Y. Tsai); Guanliang.Chen@monash.edu (G. Chen); Zach.Swiecki@monash.edu (Z. Swiecki);
Dragan.Gasevic@monash.edu (D. Gašević); John.Bradley@monash.edu (J. Bradley); Lele.Sha@monash.edu (L. Sha)
Orcid 0009-0003-9368-3460 (Z. Liang); 0000-0001-8967-5327 (Y. Tsai); 0000-0002-8236-3133 (G. Chen);
0000-0002-7414-5507 (Z. Swiecki); 0000-0001-9265-1908 (D. Gašević); 0000-0002-0356-6945 (J. Bradley);
0000-0002-8138-3853 (L. Sha)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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ki-yabarri ni-wurdu, ngabaya jibiya baji ki-yibarra ni-maliji ajinjala yinku rdiyangu
kurda kurdardi ji-wankalawu.”
“From yesterday perhaps, or maybe early this morning those handprints, the spirit
beings heard your words, when you spoke and sang for this country, so that was what
they did, they felt good, so the spirit beings from this place put their handprints in the
cave for you, they are newly made, they are not from a long time ago.”
This opening quote comes from Mavis Timothy a-Muluwamara, a senior Yanyuwa woman, the
Aboriginal owner of sections of land and waters throughout the southwest Gulf of Carpentaria,
northern Australia. Like many indigenous languages, Yanyuwa demonstrate an incredibly
intimate knowledge of their country. At the time of writing, Yanyuwa is one of the most
critically endangered indigenous languages, with only two fluent speakers in the elderly group.
Unlike the lost land, when you lose a language, a whole way of being, a whole cultural universe,
is lost forever [1, 2]. The prior revitalisation effort has shown that the establishment of a
knowledge repository (i.e., a data pool or language archive which contains critical indigenous
knowledge including encyclopedia, books, etc) is a crucial step toward language revitalisation
programme success [3, 4, 5, 6]. Since a sizeable knowledge repository effectively connects
language with critical knowledge, enriching linguistic knowledge (e.g., prefix to words, grammar
rules) with oral traditions (e.g., song, stories) and other spiritual / healing practice. Such cultural
contextualisation of a language can contribute to strengthening cultural identity and enhancing
the overall quality of language education [7, 5, 6].
While the establishment of these knowledge repositories represents a meaningful effort to pre-
serve the critical knowledge about these languages, these repositories have rarely been tailored
for practical utilisation, presenting a significant barrier for usage by the non-technical commu-
nity members to adopt them for language revitalisation (e.g., designing language educational
material) [8, 9, 10]. To tackle this, we aim to investigate the Research Question:
How can indigenous knowledge be represented and shared to foster knowledge shar-
ing between technical (e.g., computational researchers) and non-technical stake-
holders (e.g., indigenous educators, community leaders, and traditional knowledge
and land owners)?
Inspired by data storytelling, this paper employed an innovative knowledge graph (KG)
technology to construct such language repository. Different from prior approach [11, 12], the
knowledge graph models the relations of different pieces of information in a graphical format,
thereby providing semantically rich and deeply contextual knowledge about the Yanyuwa
language. We argue that such “contextualisation” paves way for narrating more meaningful
and engaging knowledge stories, which may be further polished with a “comic” layer to display
visual knowledge stories to support non-technical stakeholders’ sense making of knowledge
encoded in the KG. In doing so, we seek to bring new insights to the evolving discourse on how
to cultivate effective and inclusive language revitalisation practices for endangered indigenous
languages 1 .
1
This project is approved by Monash ethics committee, Project ID: 39279
2. Related Work
2.1. Indigenous language revitalisation
Scholars have long recognized the urgency of preserving and reviving endangered indigenous
languages, which are currently disappearing at a accelerated rate [13, 14]. Central to the
revitalisation efforts has been the exploration of innovative methods that go beyond traditional
linguistic documentation [8, 9, 15, 10]. Recent literature has increasingly emphasized the
growing potential of advanced computational models, particularly in the natural language
processing (NLP) domain [16]. For instance, in [17], the authors have systematically explored the
potential use of state-of-the-art NLP tools to assist language education to revitalise the Cherokee
language, including the use of automated question generation and language assessment material
creation. In a similar vein, Teodorescu et al. [18] explore the use of various language models in
the Cree context (an indigenous language spoken in North America), the authors concluded
that NLP tools (e.g., text classifier) may be potentially adapted for community preferences and
help with language revitalisation efforts. Despite these advances, researchers caution about the
challenges of translating nuanced cultural contexts of indigenous languages into such digital
formats [19, 20]. These insights from recent literature illustrate a growing recognition of the
need for innovative, culturally sensitive methods in the documentation and revitalisation of
endangered languages like Yanyuwa.
2.2. Knowledge graph
Recent development in computational techniques, such as knowledge graphs, open up new
possibilities in reviving the indigenous language [21, 22]. Different from prior language re-
vitalisation efforts which typically focuses on the documentation and archiving of linguistic
knowledge [23], the knowledge graph is capable of modelling the interconnectedness between
language and its cultural significance as a graphical representation. Conceptually, a knowledge
graph G is composed of the ontology O, the data graph D and the corresponding relation R
between them: G = (O, D, R), so the knowledge could be organised as the triples (“entity-
relation-entity” or “entity-property-value”) which could be scaled to a large graph network [24].
The adoption of knowledge graphs as a means to encode knowledge into practical knowledge
bases has shown considerable promise, especially in the field of linguistics. In fact, the early
establishment of knowledge graphs like WordNet [25] and ConceptNet [26] demonstrated a way
to model and analyse English language by connecting words and concepts into an intricate web
of semantic relationships, these graphs had subsequently been widely used to perform natural
language understanding tasks [27, 28, 29]. In the context of language learning and education,
the knowledge graph may also serve not just as repositories of linguistic knowledge (e.g., similar
to a dictionary) but also as an intelligent tool to encapsulate and disseminate the cultural and
contextual elements that are central to languages [30, 31, 32]. Although KG has potential to
innovate the field of language revitalisation, such technical solutions are typically constructed
by data scientists or technical personnel, and may be difficult to understand and utilise by
non-technical stakeholders, such as indigenous educators, school policy makers or parents,
whose roles are critically important to the success of a language revitalisation programme [33].
2.3. Data comics for data storytelling
To include and engage non-technical stakeholders in such KG-based language revitalisation
initiative, we resorted to the growing body of research in human-computer interaction and
data storytelling. Data storytelling is the combination of data, visuals and narrative to support
casual users or users with less experience in complex data analysis scenarios [34, 35]. Data
comics have emerged as an important visual storytelling branch to data storytelling by using
sequential images, constructed from data-driven visualisations [36]. Fundamentally, a data
comic has two dimensions: content relation and layout. Content relation describes a set of
transitions between comic panels to construct a narrative. Comic layout is the organisation of
comic panels. There are six types of content relations (i.e., narrative, temporal, faceting, visual
encoding, granular, and spatial) and nine content layouts (i.e., large panel, annotated, grouped,
tiled, parallel, grid, network, branching, and linear). These narrative components of data comics
allow for a nuanced presentation of complex information, making them particularly useful in
scenarios where understanding and participation of non-technical stakeholders are key [37].
Additionally, their visual nature supports the integration of diverse data types, from quantitative
graphs (e.g., data points) to qualitative anecdotes (e.g., narratives), offering a comprehensive
approach to data representation. In the work of [38] data comics was successfully used to explain
diabetes in a complex and multi-modal healthcare context. In a similar vein, the work presented
by [39] illustrated the potential of comics to explain complex networking data with temporal
changes. Our research builds on the growing interest in data comics and their capabilities for
effective multi-modal data communication in a language revitalisation context.
3. Methodology
3.1. Dataset
This study is based on the comprehensive Yanyuwa archive collected by John Bradley (one of the
co-authors) over a 43 year period, which includes two volume dictionary, encyclopedia of the
Yanyuwa language and digitised files of stories and songs composed and sung by contemporary
Yanyuwa men and women, image/photograph/map collections about the lives of Yanyuwa
people, narrative animations, diagrams and illustrations of material cultures. Table 1 provides a
detailed overview of the source data in this collection.
3.2. Knowledge graph construction
To fully harness the capabilities of knowledge graphs for storytelling, we strategically encode
knowledge at various levels of granularity and across multiple modalities. We argue that such
diverse array of knowledge representation may open up possibilities for more compelling blend
of visual elements that subsequently leads to better narration for engaging data comic stories.
Extracting Document and section entities. A team of three data science researchers (with no
Yanyuwa expertise) worked collaboratively to systematically extracted knowledge entities of
three granularity: document (i.e., a collection of text, audio file, or videos about the same topic,
such as storybook, songbook, or a dictionary), sections (a meaningful segment of a document,
Table 1
An overview of the comprehensive archive adopted in this study, including songs, stories, encyclopedias,
animations, documentaries, and recordings.
Type Title Description Quantity Authors/Contributors Year
Songs from the gulf country A collection of songs created by members of the Yanyuwa 24 pages Fiona Holley, Amanda Kear- 2022
community, encompassing a range of themes such as ney, Yanyuwa community,
traditions, the land, customs, rules, hunting practices, cui- and others.
sine, geography, migration, nostalgia, and land disputes.
Duwarra Wujara Kulu Buji- A story about the Journey of two young men through sa- 68 pages Yanyuwa community, John 2022
mala: Two Young Men and cred sites, encountering the Rainbow Serpent, and learn- Bradley, Amanda Kearney,
the Rainbow Serpent ing cultural norms and consequences. Brent McKee, Craig Martin,
and Others
A-barral kulu Nya- Children’s tale: ”White Cockatoo and Man” in Yanyuwa 53 pages Bella Charlie and Jemima 1982
mirningiya/A-barral kulu language. Miller
Mirningiya
Text (contains image) Kiya-arri nya-ardu ngamala/ Children’s tale: ”A Boy in the South” in Yanyuwa lan- 31 pages Bella Charlie, Jemima 1982
Ka-arri ardu ngamala guage. Miller and Queenie Simon
A Yanyuwa Encyclopaedia Part 2 of Volume 2 of the Yanyuwa Encyclopedia, serving 488 pages Yanyuwa Families; John 2017
Volume 2: Yanyuwa–English as a Yanyuwa-English Dictionary, encompasses over 4,000 Bradley; Jean Kirton; El-
Dictionary entries. freda MacDonald
A Yanyuwa Encyclopaedia Part 3 of Volume 2 of the Yanyuwa Encyclopedia, serving 129 pages Yanyuwa Families; John 2017
Volume 2: English–Yanyuwa as an English-Yanyuwa word finder, encompasses over Bradley; Jean Kirton; El-
Word Finder 4,000 entries. freda MacDonald
Yanyuwa Wuka: Language An Yanyuwa dictionary and culture resource, which could 636 pages John Bradley, Jean Kirton 1992
from yanyuwa country be considered as an earlier edition of the Yanyuwa ency- and the Yanyuwa Commu-
clopedia. nity
A Yanyuwa Encyclopaedia Volume 1 of the Yanyuwa Encyclopedia, detailing the 593 pages Yanyuwa Families; John 2016
Volume 1 Yanyuwa community’s culture, grammar, and traditional Bradley; Jean Kirton; El-
texts, including songs, poetry, and ancestral dreaming freda MacDonald
narratives.
A Yanyuwa Encyclopaedia Part 1 of Volume 2 of the Yanyuwa Encyclopedia, focusing 86 pages Yanyuwa Families; John 2017
Volume 2: Yanyuwa Gram- on Yanyuwa grammar. Bradley; Jean Kirton; El-
mar freda MacDonald
Further Aspects of the Gram- This book delves into more advanced aspects of Yanyuwa 230 pages Jean Kirton and Bella Char- 1996
Text (contains no image)
mar of Yanyuwa, Northern grammar. lie
Australia
Duwarra Wujara Kulu Buji- The Journey of two young men through sacred sites, en- 27 mins Dinah Norman a- 2022
mala: Two Young Men and countering the Rainbow Serpent, and learning cultural Manrrngaw, John Bradley,
the Rainbow Serpent norms and consequences. Brent D. McKee, and others
Turtle hunting, Tent boxing This video records the activities of Yanyuwa people re- 97 mins Trevor Graham, Sharon 1993
and Aeroplane Dance Docu- garding their turtle hunting, tent boxing and Aeroplane Connolly, Cristina Pozzan,
Video
mentary Dance. and others
Yanyuwa Culture Documen- A documentary about Yanyuwa people and their sea- 100 mins Debbie Sonenberg, 1989
tary and Journey East based culture, focusing on dugong and turtle hunting, Yanyuwa comunity,
maintaining traditions despite modernization. and others
Songs from the gulf country This animation includes various songs composed by peo- 9 mins Yanyuwa comunity, John 2022
ple from the Yanyuwa community. Bradley, Sam Pankhurst,
and others
Audio Audio Recordings A collection of 134 recordings that showcase a rich 134 files Yanyuwa community, John —
tapestry of Yanyuwa culture, including interviews, sto- Bradley, Jean Kirton and
rytelling sessions, dreaming narratives, traditional song others
performances, and conversations from the Yanyuwa peo-
ple.
such as a story within a storybook), and atomic knowledge (this may be a sentence, a phrase, or
a video frame which cannot be meaningfully reduced further). Conceptually, these knowledge
entities are “nodes” within a knowledge graph. Given the structured nature of our Yanyuwa
dataset (e.g., a storybook containing chapters of stories), we can easily extract document and
section entities via a manual segmentation (e.g., a single story within a storybook is extracted
as a section knowledge entity). For audio and video data, text transcripts were used, and
for images, captions were used. In section and atomic granularity, we excluded audio/video
without corresponding transcripts (around 62% of all audio/video data), since we cannot reliably
extract meaningful textual representation (our exploration of using Whisper tool, which is a
state-of-the-art speech recognition tool only reaches about 30% accuracy), we plan to include
these data in the next phase of this study where we conduct in-depth interview sessions with
Yanyuwa community knowledge owners and educators.
Extracting atomic entities and relations. For atomic entities, we resort to automated natural
language processing techniques as each atomic entities may be of variable length, and cannot
be easily segmented and extracted manually. The process is threefold. First, we conducted
frequency analysis using Term Frequency-Inverse Document Frequency (TF-IDF) [40], which is
a widely adopted text analytics approach to identify words / terms that are of high-importance in
a document. Second, for all entities, we conducted dependency parsing of their “title” property
(e.g., the name of a song) in the Yanyuwa dataset; this will extract the appearance of these entities
title along with their most likely dependencies (e.g., a verb which connects two entities); this
enables us to extract a large number of entity-relations in the graph. Third, to further establish
missing relations, we conducted a co-occurrence analysis involving identifying the frequency
with which entities appear together in a given dataset (not necessarily with a dependency
relation). We measured the strength of association and, for top-100 strong association, we
established the relation via manually analysing the surrounding text. A key motivation behind
these entity-relation extraction approaches is so that all the encoded entities and relations are
clearly traceable back to the source data. Alternatively, while other inference-based approaches
(e.g., GPT) may be more powerful, we opt for not using them given that they may produce
novel relations / entity names that are not traceable to the original Yanyuwa dataset and hence
compromise the authenticity of the knowledge graph. Once all the entities and relations were
established, we used Neo4j tool 2 to import them into a graphical format. A visual summative
view of the KG can be seen in Figure 1.
3.3. Storytelling workshops on knowledge graph
We invited the three researchers who developed the KG in Section 3.2 to participate in the
following design workshops. All three researchers are male, and have background in data science.
Two of the three researchers have prior knowledge about KG ontology and constructions before
participating this project. The team conducted six design workshop sessions in total.
Session 1: introduction to data comics. Given that KG researchers do not have prior expertise
in data comics, we conducted an introductory session to introduce key concepts. In the first
half-session, data storytelling fundamentals were introduced. Then, for the second half session,
data comics were introduced, including two important comic dimensions: content relation and
layout [41]. Details regarding six types of content relations and nine content layouts were
introduced (see Section 2.3). An example data comic for each layout was presented to the
participants.
Session 2: design objective. To help clarify the aim of the comic, prior to the comic design
sessions, we had an in-depth discussion about how to communicate the knowledge encoded
in KG with the non-technical stakeholders (e.g., indigenous educators), and how can data
2
Neo4j (2023). Neo4j Aura. Neo4j, Inc. https://neo4j.com/cloud/platform/aura-graph-database/.
comic help address it. All three participants agreed that this is a complex question that may
be more meaningfully tackled by first deconstruct it into smaller sub-question: i) How many
nodes should be involved in a single data comic? ii) What are the key visual techniques / data
storytelling principles in data comics that can help “narrate” a selection of nodes? iii) how
should we mitigate the risk of compromising the authenticity of the original knowledge, given
that each person may narrate the story differently and none of the participants were of Yanyuwa
background? Each participant was given the opportunity to contribute to these questions via
filling a sticky note, followed by a group discussion to resolve conflicts. The discussion resulted
in following agreement and conflicts. We reached conclusion about sub-question i and ii. For
sub-question i) although presenting KG nodes as a visual data story is context-specific, there
should be a minimum number of node involved for a sufficiently informative story, all the
node should be connected with at least one other node; sub-question ii) in data comics, the
key visual knowledge is with regard to content relation, which can be mapped from KG entity-
relation, after which, the comic layout could be determined. For sub-question 3, conflicting
view were raised, on the one hand, one participant believe that the knowledge story narration
should be based on KG-relations, therefore the visual stories should have a “ground truth” of
a set of optimal narration. On the other hand, other participants believe that, despite being
guided by the KG relation, comic design are still largely subjective given that each comic design
decision (e.g., which knowledge to focus, and which connecting node to feature, which layout
to adopt) will affect the final comic narration. As a compromise, the discussion moderator
(an experienced data storytelling researcher) decided to alternatively discuss around a set of
guidelines for designing a narrative structure based on perception theory [42, 43, 44, 45], which
are recorded in a form of design Guidelines:
• G1 [Visual perception]: Visually explain and engage the audience about the multi-modal
knowledge stories in the KG.
• G2 [Gestalt Principles]: Support recognition and sense-making via coherent and intuitive
designs principles based on proximity and continuity of knowledge.
• G3 [Affordances]: Informing KG’s use cases in language revitalisation and potentially
guide the decision making about material design process.
• G4 [Cultural context]: Produce an accurate representation of the knowledge encoded by
KG with traceability.
• G5 [Cultural context]: Explore the data comic’s potential for designing engaging material
for cultural revitalisation purposes.
Session 3: Sketching. To practice data comic design skills, a data comic researcher selected an
example KG segment consisting of 8 KG entities about an animated Yanyuwa story “Mud crab”,
consisting of 3 atomic vocabulary entities, 3 atomic image entities, and 2 story text sections.
The reason for selecting this example segment was due to that: it included a representative
set of nodes from different type of entities and modalities. KG researchers were instructed to
first think about the content relations of the entities for 20 minutes, then they selected comic
layout to sketch a comic that can best illustrate the KG segment based on design guidelines
G1-5. Feedback was given by a senior data comic researcher, and a 30-min revision session
were carried out to alter / polish the comic regarding: (i) if too little / too much information
are presented in a single layout; (ii) if the comic simply does not make sense; (iii) if misleading
visual cues were used which may cause confusion. We ensure that participants can design data
comics independently at the end of this session.
Session 4: Selecting and creating KG stories. The three KG researchers were instructed
to independently select a KG segment based on their judgement of content importance. The
segment should contain at least 3 entities and should be interconnected.
Session 5: storyboard prototyping. For the selected KG segment, participants were instructed
to independently design and construct comics using miro tool 3 . After all the participants
finished prototyping. Participants were allowed to polish / or create an alternative comic about
the same KG segment; however, only one comic was submitted per person for presentation
during session 6.
Session 6: Presentation and discussion. To reflect on the design, we organised a discussion
meeting for each participant to present their comic design and explain their design decisions.
During presentations, in addition to explaining design decisions according to guidelines G1–5,
more in-depth questions were asked to further our understanding about the KG comic design
process: (i) rationale behind the selection of KG segment with respect to narration and whether
entities should be included / removed to improve the narration; (ii) whether it is possible to
automatically translate KG relation into content relation and comic layout (e.g., ‘a Yanyuwa
vocabulary from a song’ may be translated to ‘space-annotations’ content relation, and be
visually represented using Annotated layout with vocabulary in the peripheral and song in the
middle panel); and (iii) the benefit / limitations of the use of multiple modalities (e.g., are certain
modalities preferred over the other).
4. Result
4.1. Knowledge graph
We present a summative view of KG containing all the representative entities of different types
in Figure 1. The descriptive details of all entities (i.e., “Node (Granularity)” column), their
properties (“Properties of Node”), possible relations with adjacent nodes (“Possible relations of
a Node”) and semantic category (“Possible Cattegory”) extracted using TF-IDF (see Section 3.2),
note that since atomic node (i.e., Granularity = 2) is a single atomic unit, they are automatically
assigned a category based on their connected section node.
The central of Figure 1 are all the document type node (Granularity = 0), their immediately
connected nodes are section type node (Granularity = 1), the node at the outer circle are atomic
nodes (Granularity = 2). Logically, a document node can have many section node, and a section
node can have many atomic node. Note that we have not exhaustively listed all the nodes and
all the connections as it would be too much information to display / understand clearly, and
3
Miro (2023). Miro online whiteboard. RealTimeBoard, Inc. www.miro.com.
instead, we opt for displaying the key representative node and their main structural information
(i.e., the document-section-atomic structure).
Figure 1: An overview of our knowledge graph structure, different coloured nodes represent the unique
type of each instance, the text in the grey box represents the instance name, and the text in the red box
represents the attributes adjacent to the word node.
Upon analysis, we observed following advantages of this graphical way of organising knowl-
edge: (i) Traceability. The hierarchical structure of document-section-atomic enables encoded
knowledge to be efficiently traceable to the original source, e.g., when we aim to know about
the events that occurred at a particular location, we begin by identifying the location node and
then trace back through the ‘Located at’ or ‘Happened at’ relations to explore all the events
linked to that place. Subsequently, we can further trace back through the event relations to
uncover additional knowledge about these events and the source documents, thereby facilitating
decision-making about the use case of the knowledge, for instance, the community may prefer
including stories originated / authored by people of Yanyuwa background. Besides, any exten-
sive use of the KG knowledge (e.g., in a language learning material) can be easily referred back
to the KG entity via retrieval from KG (i.e., querying the knowledge name), thereby ensuring the
authenticity of knowledge and enhancing accountability. (ii) Alignment between different
modalities. We found that, by organising knowledge of different modality in an inter-connected
manner, the knowledge of different modalities (i.e., audio, video, images, text) may be aligned
semantically. For example, when identifying key atomic entities from video animations of
Table 2
Our knowledge graph consists of a total of six types of nodes, where the granularity level of each type
of node belongs to one of the following: level 0 (the highest level), level 1 (the middle granularity level),
or level 2 (the finest granularity level).
Node (Granularity) Description Possible Relations of Node Properties of Node Possible Category
Document (0) The node of the highest granu- Include; Authored by; Con- Title; Year; Authors and Contribu- Song Poetry; General Yanyuwa
larity level (level 0). Typically it tributed by; Organized by; Edited tors; Category; Description; Top- Text; Ancestral Dreaming Text;
could be a collection of sections by ics and Keywords; Source File; Story; Reference (e.g., Grammar);
(granularity level 1) of a particu- Page Index in Source File; Source Dictionary
lar category (e.g., a collection of Stored at; Note
sections about multiple Yanyuwa
stories).
Section (1) The node of the granularity level Belong to; Include; Has; Com- Title; Year; Authors and Con- Song Poetry Text; Article Text;
1 (finer than level 0). Typically posed by; Written by; Sung by; tributors; Category; Description; Background and Information
it could be several passages of Related to Source File; Page index in Source Text; Ancestral Dreaming Text;
text centering on a specific topic File; Source Stored at; Note Story Text; Other Text
(e.g., a section could be centered
around a particular Yanyuwa po-
etry).
Location (2) The node of the finest granularity Belong to; Located at; Related to; Title; Description
level 2, particularly used for the Happened at; Part of
entity of location.
Event (2) The node of the finest granularity Happened at; Related to; Pre- Title; When; Where; Description
level 2, particularly used for the sented by; Collaboration between
entity of event.
Vocabulary (2) The node of the finest granularity Synonym; Has; type of; part of; Title; Explanation; Parts of
level 2, used for the entity of the associated with Speech
general Yanyuwa words.
Person (2) The node of the finest granularity Wrote; Composed; Sang; Is / Was; Title; Description
level 2, used for the entity of a Said
particular person.
background stories of ‘Gulf Country Song’, similar atomic entities can often be extracted from
the original song text appears in the book, thereby established a one-to-one connection between
song lyric and frames of video. In more complicated cases, an image about Hammerhead shark
appear in encyclopedia book can also be associated with a story in the storybook about the
shark with key vocabulary terms explained in the dictionary. (iii) Knowledge association and
connection. By qualitatively analysing nodes, we are able to infer latent relations between
nodes which further enhance the semantics of the encoded knowledge. For example, when we
examine the cooking Turtle series, we can deduce that some of the hunting or cooking tools
might be potentially associated with this particular event and then we can try to explore these
potential connections to broadening the scope of knowledge encompassed by it. This is further
exemplified and detailed in our data comic result, where the multi-modal comic elements may
appear from multiple sources, see Figure 4.
While KG constructed in this way has clearly demonstrated advantages in terms of content
narration, we note that the current version of KG are constructed by data scientist and have not
yet involved Yanyuwa community member to validate its correctness and comprehensiveness,
which is planned to be the next step of our study. Additionally, we also find that as KG scales
(i.e., by adding more entity-relation-entity triple), the validation of KG quality (e.g., certain
connection such as “related” may not be specific enough) becomes more difficult. Indicating a
need for exploring automated KG validation approaches.
4.2. Narrating knowledge stories
In preparation to prototyping data comics, we have conducted storytelling workshop session
1–3, from which the three KG researchers gained: (i) introductory knowledge about comics
[session 1]; (ii) the goals of what and how comic should convey key KG insights to the audience
based on a set of explicit guidelines, see Section 3.3 [session 2]; (iii) practical experience in comic
design and drawing via sketching [session 3]. We hereby continue to reporting the results in
session 4–5 about the selection and narration decision of a selected segment of KG, and how
they mapped to comic design based on the pre-determined guidelines. See Table 3, the KG
segments selected by the three participants are reported in column “Selected KG segments”,
the narration process is reported in column “Narration rationale” and the chosen data comic
relation (a.k.a “content relation” as termed in the original data comic paper) is reported in
“Comic relation”.
Table 3
An overview of the decision justification in this study includes the relations that can be translated from
content to comic, an overview of the selected KG segment, the narration process, the use of multiple
modalities and the corresponding participant
Modalities
Comic relation Selected KG segments Narration rationale Participant
Text Image Video Audio
Temporal, narrative Section node: Gulf Country Song Book Nov 1 2022, Gulf I have chosen this because I think it can help the narration " " " " Participant 2 (P2)
Country Song Book Nov 1 2022.mp4, Terns at Yinijini, process by utilizing the word nodes linked to the song
Judy Timothy a-Marrngawi, Maggie a-Bukundumara, Map. contain essential linguistic knowledge necessary to explain
Atomic node: Jarranymantharra, wangarrabantharra , Yini- the yanyuwa word. Additionally, the inclusion of an audio
jini. file enhances the conveyance of its linguistic knowledge.
The song-related image files contain the composer’s photo,
and when combined with artistic singing animations in
the video, these may facilitate the conveyance of social
knowledge.
Narrative Section node: A Yanyuwa Encyclopaedia Volume 1, A To explain the words in the article ’Hammerhead Shark’, " " Participant 3 (P3)
Yanyuwa Encyclopaedia Volume 2, Ancestral Dreaming I retrieved associated words through a KG. And aiming
Texts, Hammerhead Shark, Map. Atomic node: Warriyan- to enhance the data comic’s presentation, I incorporated
galayawu, adumu, ngurdungurdu illustrations from Yanyuwa Encyclopaedia Volume 2 based
on these connections. Considering the travel routes men-
tioned in the stories included in the article, I included a map
to help present the potential environmental knowledge.
Temporal, narrative Section node:A Yanyuwa Encyclopaedia Volume 2, turtle I selected four illustrations that vividly represent Yanyuwa " " Participant 1 (P1)
cooking series image 1, turtle cooking series image 2, tur- culture to serve as the foundation for presenting social
tle cooking series image 3, turtle cooking series image 4. knowledge. Additionally, I included word nodes associated
Atomic node: ngunduwamantharra, na-marnda, wunakaka, with these illustrations to explain the linguistic knowledge
wubantharra . embedded in the Yanyuwa vocabulary.
Overall, we observed that participants generally selected different contexts for narration
including: turtle cooking [P1], song [P2], and shark encyclopedia [P3] respectively. The contexts
selection was mainly based on: (i) the availability of imagery [P1, P2, P3] and video / audio
[P2] modalities for engagement purposes (G1 ); and (ii) perceived environmental and social
significance (G4 ). Though not explicitly, all participants followed the rule of proximity and
continuity of knowledge (G2 ) and opted for knowledge within a single context. We particularly
draw attention to P3, where the map node were extracted from Encyclopedia to complement the
Hammerhead shark story in Ancestral dreaming text, which enriched the story with a dynamic
location context (i.e., the travels over tag in the comic Figure 4). All participants emphasized the
importance of linguistic knowledge in their respective narrative (G3 ), and subsequently selected
atomic / section nodes from Yanyuwa dictionary to highlight the: (i) part of speech (e.g., noun,
verb) [P1, P2, P3]; (ii) English definition [P1, P2, P3]; and (iii) Synonym [P2];
Figure 2: Data Comic for the Turtle Cooking series with its corresponding KG segment, which is drwan
by P1.
Figure 3: Data Comic for the Yanyuwa Song ’Terns at Yinijini’ with its corresponding KG segment,
which is drwan by P2.
4.3. Data comics
In this section, we report three final comic design results obtained at Session 6 for three
selected KG segment in Table 3, respectively, as shown in Figure 2, Figure 3 and Figure 4.
Note that the comic is displayed in the left, while a visualised KG selection is displayed to
the right for traceability purposes (G4 ). On average, each comic displays 4–8 panels. We
highlight key observations in following aspects: (i) Narrative complexity. We observed a clear
difference in level of complexities in narrating on different selections of KG segment, which have
been highlighted during the comic presentation time. Specifically, all participants agreed that
including multiple modalities does not necessarily lead to narration complexity (as in Figure 1),
and, P1 (Figure 2) is the least complex while P3 (Figure 4) is the most complex. We posit that this
is related to the KG segment selection as guided by the proximity and continuity of knowledge
Figure 4: Data Comic for the Hammerhead Shark Story with its corresponding KG segment, which is
drwan by P3.
(G2 ). P2, for example noted that “if removing song translations and vocabulary panels, the comic
would be cleaner and easier to understand” when questioned about the inclusion / exclusion
of the nodes. (ii) Content relation and translation. Generally, all participants agreed that
it is possible to extract a set of generalisable rules for translating KG relation into content
relation, for instance, KG nodes that are tagged with sequential KG relations (e.g., “followed
by”) could be mapped to temporal or spatial content relations i.e., Figure 3. Similarly, relations
such as authored by, composed by may be mapped to narrative. However, given that 1 KG
relation may be mapped to multiple content relation, and 1 content relation may be mapped
to multiple comic relation, this indicated that more constraints needs to be placed to facilitate
automated KG to comic translation. (iii) Composite layouts. Apart from P1, both P2 and
P3 contains multiple layouts, i.e., P2 includes “grouped” (overall) and “tiled” layouts, while
P3 includes “Large panel” and “tiled”. However, a trade-off between clarity in overall visual
stories (G1 ) and the sense-making of individual knowledge panels (G2 ) may need to be made.
As P2 noted that “I wish to add important Yanyuwa English translations as annotations, and
potentially the more detailed definition as a new branch using branching layout, but it becomes
overly complicated.” So, while adding translations and additional vocabulary may help with
understanding the details of the song, the attention may be drifting away from the song to the
branching panels, it is for this reason that the vocabularies nodes are ultimately added as a
small tile so that the main attention is within the song content. A possible solution to mitigate
this challenge is via interactive data comics [46], i.e., the element may dynamically appear only
if user’s mouse hovers over.
5. Discussion and Future Work
In this paper, we have pioneered a study combining the power of KG with storytelling, and
situated this study in the important domain of indigenous language revitalisation. We have
investigated the use of data comic to generate visual insight about a Yanyuwa knowledge graph.
We conducted design sessions and reported our design process and final comic design.
5.1. Implications
The main implications of this study is threefold. First, given the unique advantages of knowledge
association in the KG, this may enable rapid processing of narration which translate the low-
level KG relation into more meaningful high-level content relation (as demonstrated in Table 3).
For instance, we may extract all the KG segments that are relevant to key knowledge priorities
as determined by Yanyuwa community, and use that as a basis to co-design language learning
materials with Yanyuwa language educators. The traceability of KG means that we may adopt
an iterative approach which may alter the narration past the initial stages (e.g., using a different
node in the KG instead), and revise based on feedback obtained from different community
members / knowledge owners. Second, we also find that all the KG relations are translatable
into content relations (i.e., temporal and narrative). Although, such translation may not be
one-to-one, e.g., the hammerhead shark series [P3] may also be identified as granular content
relation between the map overview, detailed map location, and appearance of hammerhead
shark. This implies that, we could potentially adopt a new approach to construct KG, by
designing a subset of more fine-grained content relations to connect entities in KG. Therefore,
KG is connected using content relation directly and may be more directly mapped into a comic
layout to support rapid and automated prototyping of comics. As part of the future work, we
may, for example, display automatically-generated comics that are highly ranked to be of high
quality to other non-technical stakeholders, while revising the low-quality comics, rather than
creating comics from scratch. Third, we noted a potential trade-off between understanding
global (the main knowledge conveyed by the comic, such as the “Terns at Yinijini” song content)
and local knowledge (the explanatory knowledge to facilitate the understanding of the main
knowledge, such as the vocabulary explanation), while the more information added can enhance
local information understanding, the global attention may be shifted to undesirable panels.
This implies that, comics displayed may need to consider reader’s prior knowledge, so as to
avoid display excessive panels, e.g., displaying related knowledge panels for all the vocabularies
which may lead to information overload.
5.2. Limitations and future work
We acknowledge following limitations. First, our KG, though being carefully constructed and
validated within the internal team, has not undergone validation by the Yanyuwa community.
The main reasoning is due to the critically endangered status of the language, and of the few
people who can still speak and understand the language (including John Bradley who is the paper
co-author, and two elderly Yanyuwa women), they lack the technical expertise in knowledge
graph. We hope the adoption of data comics can facilitate this process, and help us communicate
the KG with the non-technical stakeholders to establish a meaningful collaboration. Second, due
to copyright concerns, we have only included in our KG the data collected by John Bradley (co-
author of the paper) over a 43 period of time, we acknowledge that there are other sources which
may contain meaningful Yanyuwa knowledge data such as governmental digital repositories
including The Australian Institute of Aboriginal and Torres Strait Islander Studies. Our KG is
evolving and we will add new knowledge entities on a continual basis.
6. Acknowledgments
This research was supported by Google 2023 Award for Inclusion funding and Monash Data
Future Institute (MDFI) 2023 seed funding grant.
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