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 enables searching for disability data to expose systemic discrimination and social exclusion, which increase vulnerability to inequitable social conditions. Objective Our project aims to create an accessible and multilingual pilot disability website that structures and integrates data about people with disabilities and provides data for national and international disability advocacy communities. The platform will be endowed with a document upload function with hybrid (automated and manual) paragraph tagging, while the querying function will involve an intelligent natural language search in the supported languages. Methods We have designed and implemented a virtual community 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 following the United Nations Convention on the Rights of Persons with Disabilities (CRPD). The virtual community facilitates the uploading and sharing of validated information, and supports disability rights advocacy by enabling dissemination of knowledge. Results Using health informatics and artificial intelligence 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 website on top of a Wikibase (a Semantic Web–based datastore). The virtual community accepts 4 types of users: information producers, information consumers, validators, and administrators. The virtual community enables the uploading of documents, semiautomatic tagging of their paragraphs with meaningful keywords, and validation of the process before uploading the data to the disability Wikibase. Once uploaded, public users (information consumers) can perform a semantic search using an intelligent and multilingual search engine (QAnswer). Further enhancements of the platform are planned. Conclusions The platform ontology is flexible and can 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 virtual community supports information upload and search. Semiautomatic tagging and intelligent multilingual semantic search using natural language are enabled using artificial intelligence techniques, namely Semantic Web, machine learning, and natural language processing.
This study examines the extent to which non-health databases index public health and health care related journals. The field of public health and health care is unique and multidisciplinary and therefore presents some challenges for researchers looking for published literature in the field. These challenges compel researchers to look beyond databases such as Medline and search a wide array of databases in various fields. A list of journal titles from non-health databases in various fields was used to compare title coverage in Ovid Medline to other databases. The databases used in this study were Canadian Business & Current Affairs (CBCA) Complete which is a multidisciplinary database; ABI/Inform covering business literature; Public Affairs Information Services (PAIS); EconLit; PsycInfo focusing only on public health journals and eliminating psychology specific journals; Sociological Abstracts; and Women's Studies International.
Human rights monitoring for people with disabilities is in urgent need for disability data that is shared and available for local and international disability stakeholders (e.g., advocacy groups). Our aim is to use a Wikibase for editing, integrating, storing structured disability related data and to develop a Natural Language Processing (NLP) enabled multilingual search engine to tap into the wikibase data. In this paper, we explain the project first phase.
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