A Virtual Community for Disability Advocacy: Development of a Searchable Artificial Intelligence-Supported Platform

Area of Science:

• Health Informatics
• Artificial Intelligence
• Semantic Web
• Machine Learning
• Natural Language Processing

Background:

• Lack of disability data hinders equity monitoring and exposes individuals to discrimination and social exclusion.
• A dedicated platform is crucial for accessing disability data to address systemic inequities.
• Vulnerability to inequitable social conditions is exacerbated by the absence of comprehensive disability information.

Purpose of the Study:

• To develop an accessible, multilingual pilot website for structuring and integrating disability data.
• To provide a resource for national and international disability advocacy communities.
• To enable intelligent natural language search and document upload with hybrid tagging.

Main Methods:

• Designed and implemented a virtual community platform using Wikibase, Semantic Web, machine learning, and web programming.
• Developed a data model based on an ontology aligned with the UN Convention on the Rights of Persons with Disabilities (CRPD).
• Integrated document upload with semi-automatic paragraph tagging and a validation process.

Main Results:

• Created a pilot virtual community platform supporting disability rights advocacy through data management.
• The platform utilizes a Wikibase datastore and features a website with distinct user roles.
• Enabled document uploading, semi-automatic tagging, validation, and multilingual semantic search via QAnswer.

Conclusions:

• The platform's flexible ontology accommodates diverse disability reports and legislation, mappable to CRPD articles.
• The virtual community facilitates information upload and search, supporting disability rights advocacy.
• AI techniques (Semantic Web, ML, NLP) enable semi-automatic tagging and intelligent multilingual semantic search.