Indexing, enriching, and understanding Brazilian missing person cases from data of distributed repositories on the web
Bibliographic Data
| ID | 22937948 |
|---|---|
| Authors | Jorão Gomes (0000-0001-6712-3943, Universidade Federal de Juiz de Fora, corresponding author), Heder Soares Bernardino (0000-0003-2012-7802, Universidade Federal de Juiz de Fora), Jairo Francisco De Souza (0000-0002-0911-7980, Universidade Federal de Juiz de Fora), Enayat Rajabi (0000-0002-9557-0043, Cape Breton University) |
| Year | 2023 |
| Volume | 38 |
| Issue | 2 |
| Pages | 565-579 |
| Publication date | 2023-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | AI & Society (JOURNAL) |
| Journal identifiers | ISSN: 0951-5666 • E-ISSN: 1435-5655 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00146-022-01456-5 |
| OpenAlex | W4280497177 |
| Language | EN |
| References cited | 21 |
For decision making in government, it is necessary to have well-structured sources of information. In several countries, it is difficult to access government data as the information are dispersed, disconnected, and poorly structured. For this reason, this work presents a framework to gather, unify, and enrich missing person data from distributed web sources. The framework allows inserting new tasks specific to the user’s domain to improve data quality. In this study, Brazilian missing person data from non-governmental organizations (NGOs) and governmental websites were collected and semantically enriched. To enhance the understanding of the gathered missing people cases, we create interpretive models using machine learning techniques to extract knowledge and to encourage the use of standards for publishing the data that are frequently ignored by organizations, hindering analysis and decision-making on data. After the collection and semantic enrichment process, there was an increase of approximately 11% in the data present in the base. Also, the mining process evidenced the disappearance and reappearance of a person in Brazil according to several factors such as age, state initiatives, skin tone, hair colors, etc.
Data discovery · Data quality · Data science · Domain (mathematical analysis) · Government (linguistics) · Information retrieval · Knowledge management · Linked Data · Metadata · Missing data · Political science · Process (computing) · Publishing · Quality (philosophy) · Search engine indexing · Semantic Web · World Wide Web · Computer Science · Data Quality and Management · Engineering · Human Mobility and Location-Based Analysis · Privacy-Preserving Technologies in Data
| Highly cited | No |
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