Wikidata Support in the Creation of Rich Semantic Metadata for Historical Archives

Abstract

The research question this paper aims at answering is the following: In an ontology-driven annotation system, can the information extracted from external resources (namely, Wikidata) provide users with useful suggestions in the characterization of entities used for the annotation of documents from historical archives? The context of the research is the PRiSMHA project, in which the main goal is the development of a proof-of-concept prototype ontology-driven system for semantic metadata generation. The assumption behind this effort is that an effective access to historical archives needs a rich semantic knowledge, relying on a domain ontology, that describes the content of archival resources. In the paper, we present a new feature of the annotation system: when characterizing a new entity (e.g., a person), some properties describing it are automatically pre-filled in, and more complex semantic representations (e.g., events the entity is involved in) are suggested; both kinds of suggestions are based on information retrieved from Wikidata. In the paper, we describe the automatic algorithm devised to support the definition of the mappings between the Wikidata semantic model and the PRiSMHA ontology, as well as the process used to extract information from Wikidata and to generate suggestions based on the defined mappings. Finally, we discuss the results of a qualitative evaluation of the suggestions, which provides a positive answer to the initial research question and indicates possible improvements.

Investigating the potential of the semantic web for education: Exploring Wikidata as a learning platform

“…While reviewing the literature, it was found that the majority of existing research focuses on either technological aspects or ontological aspects of the platform. More Specifically, review of the literature illustrated that researchers use Wikidata to conduct new types of research (Amaral et al, 2021;Colla et al, 2021;Ferradji & Benchikha, 2021;Good et al, 2016;Kaffee, 2016;Konieczny & Klein, 2018;Lemus-Rojas & Odell, 2018;Li et al, 2022;Meier, 2022;Mietchen et al, 2015;Morshed, 2021;Neelam et al, 2022;Rasberry & Mietchen, 2021;Shenoy et al, 2022;Taveekarn et al, 2019;Waagmeester et al, 2020Waagmeester et al, , 2021Zhang et al, 2022). Researchers also use Wikidata to conduct new types of academic analysis in a variety of disciplines (Arnaout et al, 2021;Burgstaller-Muehlbacher et al, 2016;Kaffee et al, 2017;Klein et al, 2016;Lemus-Rojas, n.d.;Pfundner et al, 2015;Putman et al, 2017;Rutz et al, 2021;Scharpf et al, 2021a, b;Turki et al, 2019Turki et al, , 2022a.…”

A knowledge graph of interlinking digital records: the case of the 1997 Korean financial crisis

“…Research on resolving or improving digital archives using Wikidata has recently emerged (Kapsalis, 2019). Colla et al (2021) introduced a method of recommending new entities for digital materials by using named entities extracted from Wikidata. Previous research on archival metadata using knowledge graphs has typically focused on how existing vocabularies can be used to describe archival materials and enable improved discovery and access to these materials through the Web (Hawkins, 2022).…”

Designing a system to chart the development of the UK museum sector: an iterative approach

“…There has been much work on digitizing and publishing data about cultural heritage collections, for example through an integrative semantic portal (Hyvönen et al, 2005), through translation of cultural heritage metadata into linked open data (Haslhofer et al, 2011;De Boer et al, 2012;Matsumura et al, 2012;Knoblock et al, 2017) and through the development of specialist ontologies and knowledge bases (Schmitz and Black, 2008;Brownlow et al, 2015;Carriero et al, 2019). Other works on digitizing museum-related information include sharing of museum visit experiences via web and smartphone apps, digital bookmarking, or real-time video (Kostoska et al, 2013;Pisoni et al 2020), digitization of historical archives (Colla et al, 2021), and application of machine-learning approaches to automatically generate cultural heritage content or metadata utilising public resources such as Wikipedia and Wikidata (De Benedictis et al, 2021;Colla et al, 2021). In contrast to such works, the MM project aims to support experts' research into the history, status and development of a whole museum sector.…”