ICT for Genealogical Data

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: 31 March 2024 | Viewed by 1963

Special Issue Editors

Institute of Informatics and Telematics – National Research Council (IIT-CNR), 56124 Pisa, Italy
Interests: open data; data visualization; data science; web applications; cartographic mapping techniques
Special Issues, Collections and Topics in MDPI journals
Institute of Informatics and Telematics – National Research Council (IIT-CNR), 56124 Pisa, Italy
Interests: data science; data narrative; web applications; machine learning; cultural heritage; tourism
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The building of genealogical trees has become a popular activity carried out by millions of people, including both hobbyists and scholars. Thanks to the online publication of local official registries of births, marriages, and deaths, new scenarios and perspectives have been opened to enrich genealogical family trees. On the one hand, there are many potentialities given by the huge volume of data, such as the building of very complex genealogical trees covering many centuries of history. On the other hand, the management of large genealogical trees also involves new challenges in the collection, quality, accuracy, storage, querying, analysis, and visualization of genealogical data. This Special Issue calls for research articles on novel approaches to genealogical data management. Both conceptual and practical papers describing challenges and perspectives are also welcome. Original contributions that report on real experiences and scenarios in the usage of any kind of genealogical data are also encouraged. While not excluding the possibility of presenting works on other topics regarding ICT for genealogical data, the specific topics of interest in this Special Issue are the following:

  • Named entity recognition and linking to extract genealogical data from documents;
  • Handwritten recognition of structured or semi-structured registries of births, marriages and deaths;
  • Data storage and efficient queries of large genealogical trees;
  • Quality issues and techniques to improve the accuracy of genealogical data, such as data cleaning;
  • Reconciliation of different genealogical data sources;
  • Copyright issues related to the publication and use of genealogical trees;
  • Machine learning and deep learning analysis of genealogical data;
  • Issues, challenges, and solutions related to the visualization of a genealogical tree;
  • Analysis of new sources of potential genealogical data including social networks;
  • Use of big genealogical data to study social phenomena, including migration, social changes, educational levels, wars, epidemics, natural disasters and so on;
  • Use of big genealogical data for pathological studies, such as the transmission of genetic diseases, familiarity in diseases, and so on.

Dr. Andrea Marchetti
Dr. Angelica Lo Duca
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • genealogical data
  • family tree
  • genealogical data mining
  • genealogical data analysis
  • genealogical data visualization
  • open data
  • social science
  • genealogical data linking
  • digital humanities
  • knowledge graph
  • information visualization
  • genealogical data format

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

16 pages, 8768 KiB  
Article
Genealogical Data Mining from Historical Archives: The Case of the Jewish Community in Pisa
by Angelica Lo Duca, Andrea Marchetti, Manuela Moretti, Francesca Diana, Mafalda Toniazzi and Andrea D’Errico
Informatics 2023, 10(2), 42; https://doi.org/10.3390/informatics10020042 - 11 May 2023
Cited by 1 | Viewed by 1470
Abstract
The Jewish community archive in Pisa owns a vast collection of documents and manuscripts that date back centuries. These documents contain valuable genealogical information, including birth, marriage, and death records. This paper aims to describe the preliminary results of the Archivio Storico della [...] Read more.
The Jewish community archive in Pisa owns a vast collection of documents and manuscripts that date back centuries. These documents contain valuable genealogical information, including birth, marriage, and death records. This paper aims to describe the preliminary results of the Archivio Storico della Comunita Ebraica di Pisa (ASCEPI) project, with a focus on the extraction of data from the Nati, Morti e Ballottati (NMB) Registry document in the archive. The NMB Registry contains about 1900 records of births, deaths, and balloted individuals within the Jewish community in Pisa. The study uses a semiautomatic pipeline of digitization, transcription, and Natural Language Processing (NLP) techniques to extract personal data such as names, surnames, birth and death dates, and parental names from each record. The extracted data are then used to build a knowledge base and a genealogical tree for a representative family, Supino. This study demonstrates the potential of using NLP and rule-based techniques to extract valuable information from historical documents and to construct genealogical trees. Full article
(This article belongs to the Special Issue ICT for Genealogical Data)
Show Figures

Figure 1

Back to TopTop