Semantic enrichment for enhancing LAM data and supporting digital humanities. Review article

With the rapid development of the digital humanities (DH) field, demands for historical and cultural heritage data have generated deep interest in the data provided by libraries, archives, and museums (LAMs). In order to enhance LAM data’s quality and discoverability while enabling a self-sustaining ecosystem, “semantic enrichment” becomes a strategy increasingly used by LAMs during recent years. This article introduces a number of semantic enrichment methods and efforts that can be applied to LAM data at various levels, aiming to support deeper and wider exploration and use of LAM data in DH research. The real cases, research projects, experiments, and pilot studies shared in this article demonstrate endless potential for LAM data, whether they are structured, semi-structured, or unstructured, regardless of what types of original artifacts carry the data. Following their roadmaps would encourage more effective initiatives and strengthen this effort to maximize LAM data’s discoverability, use- and reuse-ability, and their value in the mainstream of DH and Semantic Web

Semantic Enrichment of a Multilingual Archive with Linked Open Data

This paper introduces MERCKX, a Multilingual Entity/Resource Combiner & Knowledge eXtractor. A case study involving the semantic enrichment of a multilingual archive is presented with the aim of assessing the relevance of natural language processing techniques such as named-entity recognition and entity linking for cultural heritage material. In order to improve the indexing of historical collections, we map entities to the Linked Open Data cloud using a language-independent method. Our evaluation shows that MERCKX outperforms similar tools on the task of place disambiguation and linking, achieving over 80% precision despite lower recall scores. These results are encouraging for small and medium-size cultural institutions since they demonstrate that semantic enrichment can be achieved with limited resources.Peer reviewe

Efficient and effective OCR engine training

We present an efficient and effective approach to train OCR engines using the Aletheia document analysis system. All components required for training are seamlessly integrated into Aletheia: training data preparation, the OCR engine’s training processes themselves, text recognition, and quantitative evaluation of the trained engine. Such a comprehensive training and evaluation system, guided through a GUI, allows for iterative incremental training to achieve best results. The widely used Tesseract OCR engine is used as a case study to demonstrate the efficiency and effectiveness of the proposed approach. Experimental results are presented validating the training approach with two different historical datasets, representative of recent significant digitisation projects. The impact of different training strategies and training data requirements is presented in detail

Fairness and transparency throughout a digital humanities workflow: Challenges and recommendations

How can we achieve sufficient levels of transparency and fairness for (humanities) research based on historical newspapers? Which concrete measures should be taken by data providers such as libraries, research projects and individual researchers? We approach these questions from the vantage point that digitised newspapers are complex sources with a high degree of heterogeneity caused by a long chain of processing steps, ranging, e.g., from digitisation policies, copyright restrictions to the evolving performance of tools for their enrichment such as OCR or article segmentation. Overall, we emphasise the need for careful documentation of data processing, research practices and the acknowledgement of support from institutions and collaborators

Semantic enrichment for enhancing LAM data and supporting digital humanities. Review article

With the rapid development of the digital humanities (DH) field, demands for historical and cultural heritage data have generated deep interest the data provided by libraries, archives, and museums (LAMs). In order to enhance LAM data’s quality and discoverability while enabling a self-sustaining ecosystem, “semantic enrichment” becomes a strategy increasingly used by LAMs during recent years. This article introduces a number of semantic enrichment methods and efforts that can be applied to LAM data at various levels, aiming to support deeper and wider exploration and use of LAM data in DH research. The real cases, research projects, experiments, and pilot studies shared in this article demonstrate endless potential for LAM data, whether they are structured, semi-structured, or unstructured, regardless of what types of original artifacts carry the data. Following their roadmaps would encourage more effective initiatives and strengthen this effort to maximize LAM data’s discoverability, use- and reuse-ability, and their value in the mainstream of DH and Semantic Web

Evaluation von Volltextdaten mit Open-Source-Komponenten

Im Bereich der Volltexterzeugung stehen heute vollwertige Open-Source Systeme zur Verfügung. Auch bei der Auswertung der Resultate können etablierte Open-Source-Werkzeuge aus den Bereichen Data Science (DS), Information Retrieval (IR) und Natural Language Processing (NLP) eingesetzt werden. Nach einer kurzen Vorstellung üblicher Auswertungsverfahren und Metriken wird exemplarisch über den Einsatz dieser Tools im DFG-Projekt „Digitalisierung Historischer Deutscher Zeitungen I“ der Universitäts- und Landesbibliothek Sachsen-Anhalt (ULB) berichtet.In the area of full text recognition, several fully-fledged open source systems are available today. Established open source tools stemming from the fields of Data Science (DS), Information Retrieval (IR) and Natural Language Processing (NLP) can also be used to evaluate the results. After a brief discussion of common evaluation procedures and metrics, the application of such tools in the DFG-funded project „Digitisaion of historical German newspapers I (Digitalisierung Historischer Deutscher Zeitungen I)“ at the University and State Library Saxony-Anhalt is used as an example

Semantic enrichment for enhancing LAM data and supporting digital humanities. Review article

With the rapid development of the digital humanities (DH) field, demands for historical and cultural heritage data have generated deep interest in the data provided by libraries, archives, and museums (LAMs). In order to enhance LAM data’s quality and discoverability while enabling a self-sustaining ecosystem, “semantic enrichment” becomes a strategy increasingly used by LAMs during recent years. This article introduces a number of semantic enrichment methods and efforts that can be applied to LAM data at various levels, aiming to support deeper and wider exploration and use of LAM data in DH research. The real cases, research projects, experiments, and pilot studies shared in this article demonstrate endless potential for LAM data, whether they are structured, semi-structured, or unstructured, regardless of what types of original artifacts carry the data. Following their roadmaps would encourage more effective initiatives and strengthen this effort to maximize LAM data’s discoverability, use- and reuse-ability, and their value in the mainstream of DH and Semantic Web