WikiBio: a Semantic Resource for the Intersectional Analysis of Biographical Events

Biographical event detection is a relevant task for the exploration and comparison of the ways in which people's lives are told and represented. In this sense, it may support several applications in digital humanities and in works aimed at exploring bias about minoritized groups. Despite that, there are no corpora and models specifically designed for this task. In this paper we fill this gap by presenting a new corpus annotated for biographical event detection. The corpus, which includes 20 Wikipedia biographies, was compared with five existing corpora to train a model for the biographical event detection task. The model was able to detect all mentions of the target-entity in a biography with an F-score of 0.808 and the entity-related events with an F-score of 0.859. Finally, the model was used for performing an analysis of biases about women and non-Western people in Wikipedia biographies.</p

LOD Navigator: Tracing Movements of Italian Shoah Victims

In this work, we present LOD Navigator, a data visualisation and exploration tool to track the lives and trajectories of Italian Shoah Victims. We take advantage of the work done at the Contemporary Jewish Documentation Center in Milan (CDEC), leading to the publication of a database of Linked Open Data (LOD) containing information about the life and persecution of each victim. Such database was then enriched semi-automatically and uploaded in the LOD Navigator, giving the possibility of getting new insight into collective traits of the Italian Shoah tragedy and into personal stories of victims. Information is now available and can be navigated in an intuitive and interactive way

Analyzing biography collections historiographically as Linked Data : Case National Biography of Finland

Biographical collections are available on the Web for close reading. However, the underlying texts can also be used for data analysis and distant reading, if the documents are available as data. Such data is usable for creating intelligent user interfaces to biographical data, including Digital Humanities tooling for visualizations, data analysis, and knowledge discovery in biographical and prosopographical research. In this paper, we re-use biographical collection data from a historiographical perspective for analyzing the underlying collection. For example: What kind of people have been included in the collection? Does the language used for describing female biographees differ from that for men? As a case study, the Finnish National Biography, available as part of the Linked Open Data service and semantic portal BiographySampo - Finnish Biographies on the Semantic Web is used. The analyses show interesting results related to, e.g., how specific prosopographical groups, such as women or professional groups are represented and portrayed. Various novel statistics and network analyses of the biographees are presented. Our analyses give new insights to the editors of the National Biography as well as to researchers in biography, prosopography, and historiography. The presented approach can be applied also to similar biography collections in other countries.Peer reviewe

ramble on tracing movements of popular historical figures

We present RAMBLE ON, an application integrating a pipeline for frame-based information extraction and an interface to track and display movement trajectories. The code of the extraction pipeline and a navigator are freely available; moreover we display in a demonstrator the outcome of a case study carried out on trajectories of notable persons of the XX Century

Geographical Research in the Digital Humanities: Spatial Concepts, Approaches and Methods

The richness of social and cultural theory in the humanities offers countless opportunities for using theory-informed concepts in data-based analysis workflows. The contributors to this volume thus encourage further research utilizing out-of-the-box models and approaches to space and place in the field of Digital Humanities. The collection follows the two complementary goals of providing promising conceptualisations of space and place for a broad audience from Digital Humanities, and of presenting current work in Digital Humanities using different conceptualisations of space and place or offering innovative methods for their analysis

Biographical information extraction: A language-agnostic methodology for datasets and models

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Information extraction (IE) refers to the task of detecting and linking information contained in written texts. While it includes various subtasks, relation extraction (RE) is used to link two entities in a text via a common relation. RE can therefore be used to build linked databases of knowledge across a wide area of topics. Today, the task of RE is treated as a supervised machine learning (ML) task, where a model is trained using a specific architecture and a specific annotated dataset. These specific datasets typically aim to represent common patterns that the model is to learn, albeit at the cost of manual annotation, which can be costly and time-consuming. In addition, due to the nature of the training process, the models can be sensitive to a specific genre or topic, and are generally monolingual. It therefore stands to reason, that certain genres and topics have better models, as they are treated with a higher priority due to financial interests for instance. This in turn leads to RE models not being available to every area of research, leaving incomplete linked databases of knowledge. For instance, if the birthplace of a person is not correctly extracted, the place and the person can not be linked correctly, therefore not leaving linked databases incomplete. To address this problem, this thesis explores aspects of RE that could be adapted in ways which require little human effort, therefore making RE models more widely available. The first aspect is the annotated data. During the course of this thesis, Wikipedia and its subsidiaries are used as sources to automatically annotate sentences for RE. The dataset, which is aimed towards digital humanities (DH) and historical research, is automatically compiled by aligning sentences from Wikipedia articles with matching structured data from sources including Pantheon and Wikidata. By exploiting the structure of Wikipedia articles and robust named entity recognition (NER), information is matched with relatively high precision in order to compile annotated relation pairs for ten different relations that are important in the DH domain: birthdate, birthplace, deathdate, deathplace, occupation, parent, educated, child, sibling and other (all other relations). Furthermore, the effectiveness of the dataset is demonstrated by training a state-of-the-art neural model to classify relation pairs. For its evaluation, a manually annotated gold standard set is used. An investigation of the necessary adaptations to recreate the automatic process in a multilingual setting is also undertaken, looking specifically at English and German, for which similar neural models are trained and evaluated on a gold standard dataset. While the process is aimed here at training neural models for RE within the domain of digital humanities and history, it may be transferable to other domains