JMIR Publications

ABSTRACT

Background:

The lack of availability of disability data has been identified as a major challenge hindering continuous disability equity monitoring. It is important to develop a platform that allows search for disability data to expose systemic discrimination and social exclusion that increase vulnerability to inequitable social conditions.

Objective:

Our project aims to create an accessible multilingual disability pilot website that structures and integrates data about people with disability and serves national and international disability advocacy communities. The data storage will be endowed with a document upload function with hybrid (automated and manual) paragraph tagging and the querying function will be an intelligent natural language search capability in the supported languages.

Methods:

We have designed and implemented a virtual community (VC) platform using wikibase, semantic web, machine learning and web programming tools to enable disability communities to upload and search for disability documents. The platform data model is based on an ontology we have designed after the UN Convention on the Rights of Persons with Disabilities (CRPD). The VC facilitates validated information uploading and sharing, and support of disability rights advocacy by enabling dissemination of knowledge.

Results:

Using health informatics and AI techniques (namely Semantic Web, Machine Learning and Natural Language Processing techniques) we were able to develop a pilot virtual community that supports disability rights advocacy by facilitating uploading, sharing, and accessing disability data. The system consists of a web site on top of a Wikibase (a Semantic Web-based data store). The VC accepts four types of users: information producers, information consumers, validators, and administrators. The VC permits upload of documents, semi-automatic tagging of their paragraphs with meaningful keyworks, and validation of the process before uploading the data to the disability wikibase. Once uploaded public users (information consumers) can perform semantic search using an intelligent and multilingual search engine (QAnswer). Further enhancements of the platform are planned.

Conclusions:

The platform ontology is flexible to accommodate advocacy reports and disability policy and legislation from specific jurisdictions, which can be accessed in relation to the CRPD articles. The platform ontology can be expanded to fit international contexts. The VC support information upload and search. Semi-automatic tagging and intelligent multilingual semantic search using natural language are enabled using AI techniques, namely Semantic Web, Machine Learning and Natural Language processing.