Disability Advocacy using a Smart Virtual Community
Bushra Kundi
1a
, Dhayananth Dharmalingam
2b
, Rediet Tadesse
2c
, Alexandra Creighton
1d
,
Rachel Gorman
1e
, Pierre Maret
2,5 f
, Fabrice Muhlenbach
2g
, Alexis Buettgen
1h
,
Enakshi Dua
3i
, Thumeka Mgwigwi
4j
, Serban Dinca-Panaitescu
1k
and Christo El Morr
1l
1
School of Health Policy and Management, York University, Keele Street, Toronto, Canada
2
Université Jean Monnet Saint Etienne, France
3
School of Gender, Sexuality and Women's Studies, York University, Canada
4
Student Learning and Academic Success Department, York University Libraries, York University, Toronto, ON, Canada
5
The QA Company, Saint Etienne, France
Keywords: Health Informatics, Machine Learning, Semantic Web, Natural Language Processing, Disability, Critical
Disability, Wikibase, Disability Advocacy, Disability Rights.
Abstract: The lack of readily available disability data is a major barrier for disability advocacy globally. The collection
and access to disability data is crucial to address social inequities, discrimination, and human rights violations
within the disability community. The Disability Wiki project intends to use AI techniques such as Machine
Learning and Semantic Web to extract and store existing disability-related data into one platform (Wikibase)
and to provide a multilingual natural language enabled search engine and a screen-reader-accessible for its
users.
1 INTRODUCTION
The lack of availability of disability data is a major
barrier for disability advocacy and disability rights
monitoring globally. Disability advocacy information
and data is not readily available which can further
delay important projects and commissions by human-
rights and non-governmental organizations (NGO)
(Loeb, 2013).
It is vital to have readily available structured and
unstructured disability data from a critical disability
a
https://orcid.org/0000-0001-9581-8531
b
https://orcid.org/0000-0002-4682-8992
c
https://orcid.org/0000-0002-7179-5555
d
https://orcid.org/0000-0002-7573-221X
e
https://orcid.org/0000-0003-3144-1733
f
https://orcid.org/0000-0001-5437-7725
g
https://orcid.org/0000-0002-1825-4290
h
https://orcid.org/0000-0002-7370-723X
i
https://orcid.org/0000-0003-2554-2028
j
https://orcid.org/0000-0001-9757-8084
k
https://orcid.org/0000-0003-2620-4389
l
https://orcid.org/0000-0001-6287-3438
perspective to track systemic discrimination, social
exclusion, adverse socioeconomic outcomes, and
social inequity for persons with disabilities. Health
informatics (El Morr, 2014, 2018) and artificial
intelligence techniques (Akerkar et al., 2012;
Diefenbach et al., 2017; Diefenbach et al., 2019; El
Morr & Ali-Hassan, 2019b; Gorman et al., 2021;
Vercouter & Maret, 2012) provide helpful tools to
organize, store and search for disability data using
Natural Language Processing (NLP). Also, virtual
communities that allow remote members to
316
Kundi, B., Dharmalingam, D., Tadesse, R., Creighton, A., Gorman, R., Maret, P., Muhlenbach, F., Buettgen, A., Dua, E., Mgwigwi, T., Dinca-Panaitescu, S. and El Morr, C.
Disability Advocacy using a Smart Virtual Community.
DOI: 10.5220/0010748600003123
In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 316-319
ISBN: 978-989-758-552-4; ISSN: 2184-4305
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

collaborate over a task have been used successfully in
health (El Morr, 2010; El Morr et al., 2017) and search
techniques benefited from artificial intelligence to
uncover bibliographical data (Muhlenbach & Lallich,
2010; Muhlenbach & Sayn, 2019).
The Disability Wiki project aims to create a virtual
community consisting of a multilingual web site with
screen-reader-accessibility and a semantic data
storage (wikibase). The system is endowed with a
document upload function with hybrid (automated
and manual) paragraph tagging and the querying
function implements an intelligent natural language-
based search system.
The Disability Wiki project includes 3 phases:
ontology development and fine tuning (Phase I),
Wikibase and search engine design, development,
and testing (Phase II), use and use analysis (Phase
III). Phases I and II are presented below
2 METHODS
A computer ontology describing the Convention on
the Rights of Persons with Disabilities (CRPD) was
developed through constant deliberation and
coordination between experts in the field of critical
disability studies, health informatics and computer
science. The ontology development process was
iterative and allowed the critical disability studies
experts and health informatics and computer science
professionals to fine tune and clarify information
description and user requirements. The development
of the ontology served as a blueprint for the Disability
Wiki website.
Background technology used included use of
QAnswer, a platform that makes Resource
Description Framework (RDF) data accessible via
natural language. QAnswer enables intelligent search
using intelligent natural language questioning
(Diefenbach et al., 2020; Diefenbach et al., 2019;
QAnswer, 2021). QAnswer is the first artificial
intelligence (AI) driven platform to query RDF data
stores in natural language using semantic
technologies (QAnswer, 2021). Python and R were
both used to prepare and program machine learning
models(El Morr & Ali-Hassan, 2019a).
2.1 Glossary & File Tagging
A set of disability rights reports were chosen to train
the software for knowledge acquisition, i.e. paragraph
splitting and tagging. A glossary of tag terms was then
created by experts in the field of health informatics and
critical disability to aid in tagging the disability rights
reports. Synonyms were then added to represent a wide
variety of tag terms. The glossary was continuously
edited and fine-tuned to meet the standards of the
disability rights reports and critical disability studies
field until expert satisfaction was reached.
To ensure knowledge acquisition, paragraphs
were extracted from disability rights reports and a
machine learning model was trained to predict the
paragraphs’ semantic meaning and tag it with the
appropriate glossary terms.
Extracted paragraphs were initially automatically
tagged using the glossary, then manually re-checked,
and re-tagged to train the machine learning model
responsible for automatic paragraph tagging.
2.2 Disability Wiki Website
The Disability Wiki virtual community website was
developed on top of an instance of a Wikibase
dedicated to the data storage). It allowed for
authenticated users (i.e., information producers) to
upload disability documents and use the machine
learning to tag their paragraphs. Information producers
could delete or add tags if they deem necessary, and the
new tag set can serve for new training of the machine
learning model responsible of tagging.
Once tagging is done the administrator can push
the documents and tags to the wikidata storage.
Using the website search feature, public users can
access disability data and information with Natural
Language questions. No authentication is needed for
public users.
3 RESULTS
The CRPD ontology described relationships between
entities in the CRPD. For instance, the CRPD is a
convention developed by the United Nations and is
composed of several articles, each article has one or
several topics. Figure 1 shows a partial view of the
ontology.
Figure 1: Partial view of the CRPD ontology.
Disability Advocacy using a Smart Virtual Community
317

Figure 2 shows part of the glossary that was
created for tagging the disability rights reports.
Column A shows the primary tags that were used to
tag paragraphs while the synonyms representing the
terms are in the columns that follow.
Figure 2: Glossary.
Figure 3 shows a page of the Disability Wiki
project web site. The image shows the search bar with
examples of questions.
Figure 3: Disability Wiki Web Page.
The disability wiki website uses QAnswer API
endpoints to send the questions to and retrieve
answers from the disability wiki.
Figure 4: Search function flow of the website.
4 DISCUSSIONS
The multidisciplinary nature of critical disability
studies, health informatics and computer science
required the researchers to deliberate and coordinate
on every stage of the project. To reach a mutual
agreement, the project required multiple meeting
sessions and re-iteration between researchers for
unexpected delays, deadlines, clarification, or
challenges.
In the domain of critical disability studies, we are
not aware of any other tool or effort for collecting and
giving easy access to related data. Our proposal
therefore responds to this need.
Giving access to data through the Web is of course
not new. However very topic-focused data is too
specific to be adequately made accessible by
generalist search engines such as google. Also, the
online encyclopedia Wikipedia.org is not suited
because it requires editing documents, which is not
the case on our platform (document upload).
The web site www.enslaved.org is an example of
domain related data collection, however document
upload is not proposed, nor automated tagging or
semantic free text search.
Future evaluation for the use of the virtual
community using technology Technology
Acceptance Model or the RE-AIM Framework
would be important to understand users acceptance
(El Morr et al., 2017). A user interface evaluation is
underway to ensure accessibility of the platform.
Finally, the platform supports English and French,
and is already expanding in scope as we are currently
adding data and documents pertaining to Black
Disability Studies and Disability Justice.
5 CONCLUSIONS
Readily available and easy to access disability data is
an essential need today. The social model of critical
disability allows us to see and address the
discrepancies, discrimination, and inequities in the
system for persons with disabilities.
The Disability Wiki virtual community uses AI
techniques to easily integrate and index existing
disability data into one accessible platform as well as
allowing natural language searching for its’ users.
The platform is currently in a pilot stage.
ACKNOWLEDGEMENTS
The authors acknowledge and thank The QA
Company for providing access to their technology.
FUNDING
The work reported in this paper was funded by the
Social Sciences and Humanities Research Council
(SSHRC), Insight Development Grant; Grant number
430-2020-00819.
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