Mining Ministers (1572–1815). Using Semi-structured Data for Historical Research

Braake, S. ter; Fokkens, Antske; Lieburg, F.A. van

doi:10.1007/978-3-319-15168-7_35

Cited by 4 publications

(3 citation statements)

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“…Our work on biographical linked data started 2013 by designing a demonstrator [38] based on the short biographies of the National Biography of Finland (NBF) 20 . The research hypotheses of this system was that "the reading experience can be enhanced by enriching the biographies with additional life time events, by proving the user with a spatio-temporal context for reading, and by linking the text to additional contents in related datasets".…”

Section: Generations Of Publishing Biographiesmentioning

confidence: 99%

“…Aside publishing biographical dictionaries in print and on the Web, representing and analyzing biographical data has grown into a new research and application field. In 2015, the first Biographical Data in Digital World workshop BD2015 was held presenting several works on studying and analyzing biographies as data [20], and the proceedings of BD2017 contain more similar works [21]. In [22], analytic visualizations were created based on U.S. Legislator registry data.…”

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confidence: 99%

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Creating and Using Biographical Dictionaries for Digital Humanities Based on Linked Data: A Survey of Web Services in Use in Finland

Hyvönen

2024

Biographical Data in a Digital World 2022

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Section: Generations Of Publishing Biographiesmentioning

confidence: 99%

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confidence: 99%

Creating and Using Biographical Dictionaries for Digital Humanities Based on Linked Data: A Survey of Web Services in Use in Finland

Hyvönen

2024

Biographical Data in a Digital World 2022

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“…In the field of prosopography (i.e. the collective study of individual biographies), this combined information enables tracing different individuals through time and space, as reported in Buning (2016)(23) and Braake et al (2014)(95). It is also crucial to be able to discern between different person names and toponyms in the task of historical event extraction.…”

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confidence: 98%

Entity-Centric Text Mining for Historical Documents

Ardanuy¹

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Recent years have seen an important increase of digitization projects in the cultural heritage domain. As a result, growing efforts have been directed towards the study of natural language processing technologies that support research in the humanities. This thesis is a contribution to the study and development of new text mining strategies that allow a better exploration of contemporary history collections from an entity-centric perspective. In particular, this thesis focuses on the challenging problems of disambiguating two specific kinds of named entities: toponyms and person names. They are approached as two clearly differentiated tasks, each of which exploiting the inherent characteristics that are associated to each kind of named entity.Finding the correct referent of a toponym is a challenging task, and this difficulty is even more pronounced in the historical domain, as it is not uncommon that places change their names over time. The method proposed in this thesis to disambiguate toponyms, GeoSem, is especially suited to work with collections of historical texts. It is a weakly-supervised model that combines the strengths of both toponym resolution and entity linking approaches by exploiting both geographic and semantic features. In order to do so, the method makes use of a knowledge base built using Wikipedia as a basis and complemented with additional knowledge from GeoNames.The method has been tested on a historical toponym resolution benchmark dataset in English and improved on the state of the art. Furthermore, five datasets of historical news in German and Dutch have been created from scratch and annotated. The method proposed in this thesis performs significantly better on them than two out-of-the-box state-of-the-art entity linking methods when only locations are considered for evaluation. This dissertation would not have been completed without the support and encouragement of many people. First and foremost, I wish to thank my supervisor, Professor Caroline Sporleder, from whom I have learned much, for her trust, dedicated support and guidance during these years. I would also like to thank Professor Ramin Yahyapour, for accepting to be part of this thesis committee and sharing with me some insightful comments to improve the structure of this dissertation. I would also like to gratefully acknowledge Professors Ulrich Heid, Dieter Hogrefe, Gerhard Lauer, and Wolfgang May, for their willingness to serve on my examination committee.

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Extracting Biographical Spatial Timelines: Corpus and Experiments

Vempala

Blanco

2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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Mining Ministers (1572–1815). Using Semi-structured Data for Historical Research

Cited by 4 publications

References 1 publication

Creating and Using Biographical Dictionaries for Digital Humanities Based on Linked Data: A Survey of Web Services in Use in Finland

Creating and Using Biographical Dictionaries for Digital Humanities Based on Linked Data: A Survey of Web Services in Use in Finland

Entity-Centric Text Mining for Historical Documents

Extracting Biographical Spatial Timelines: Corpus and Experiments

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