2023, Eichler, Frederik, Eppelin, Anita, Kampkaspar, Dario, Schrader, Antonia C., Söllner, Konstanze, Vierkant, Paul, Withanage, Dulip, Wrzesinski, Marcel

In der vorliegenden Handreichung stellen wir unterschiedliche technische Ressourcen vor, die redaktionelle Arbeiten unterstützen können. Dabei empfiehlt es sich, Software und Systeme zu nutzen, die den Wandel hin zu einer offenen, niederschwelligen und nachhaltigen Wissenschaftskultur fördern. Hierzu zählt in erster Linie die Verwendung von Open-Source-Software. Unsere Empfehlungen haben dabei eine begrenzte Reichweite: Serviceanbieter, Software und Projekte sind zu einem späteren Zeitpunkt ggf. nicht mehr verfügbar. Auch sind gerade die Infrastruktureinrichtungen in das föderale Wissenschaftssystem integriert, was sie bestimmten Unwägbarkeiten aussetzt.

2020, Keck, Ingo R., Vidal, Maria-Esther, Heller, Lambert, Mikroyannidis, Alexander, Chang, Maiga, White, Stephen

In this article, we look at the challenges that arise in the use and management of education credentials, and from the switch from analogue, paper-based education credentials to digital education credentials. We propose a general methodology to capture qualitative descriptions and measurable quantitative results that allow to estimate the effectiveness of a digital credential management system in solving these challenges. This methodology is applied to the EU H2020 project QualiChain use case, where five pilots have been selected to study a broad field of digital credential workflows and credential management. Copyright (c) IARIA, 2020

2012, Heller, Lambert, Nissen, Martin, Schade, Frauke, Umlauf, Konrad

Seit Beginn der 2000er Jahre haben Open Access und Open Content in der global vernetzten Informationswelt sprunghaft an Bedeutung gewonnen. Die Verfügbarkeit von Wissen ist zu einem entscheidenden Entwicklungsfaktor für Wissenschaft, Wirtschaft, Kultur und Gesellschaft geworden. Angesichts der wachsenden Bedeutung stellen sich Fragen der Verfügbarkeit sowie der Teilhabe auf regionaler, nationaler und globaler Ebene. Der freie Zugang zu Wissen erhöht nachweislich dessen Sichtbarkeit. Er ist Voraussetzung für technische Erschließungsverfahren und eröffnet neue Möglichkeiten der Auffindbarkeit und automatisierten Verknüpfung. Aus wissenspolitischer Perspektive trägt Open Access dazu bei, die drohende Kluft zwischen öffentlich geförderten Informationseinrichtungen in regionalen und nationalen Informationsverbünden zu verringern, zudem ermöglicht Open Access strukturell unterentwickelten Regionen, am wissenschaftlichen und wirtschaftlichen Fortschritt teilzuhaben. Formelle Unterstützung findet Open Access durch führende Wissenschafts- und Fördereinrichtungen wie beispielsweise die Deutsche Forschungsgemeinschaft, die Europäische Kommission sowie den Weltverband der Bibliotheken IFLA. Im wissenschaftlichen Bereich wird Open Access in Forschungseinrichtungen in den USA, Großbritannien und der Schweiz durch sog. Mandatories (i. e. rechtsverbindliche Verpflichtungen) getragen, die von ihren Wissenschaftlern die Publikation unter Open-Access-Bedingungen einfordern. Hierbei gibt es jedoch erhebliche nationale Unterschiede, die auf verschiedenen Rechtssystemen und Publikationstraditionen gründen. Der Fokus dieses Beitrags ist darauf gerichtet, wie Wissenschaftliche und Öffentliche Bibliotheken Open Access, Open Content und gemeinfreies Kulturerbe erwerben, bereitstellen, in ihre vorhandenen digitalen Systeme einbinden und unterstützen können. Die Möglichkeiten zukünftiger Tätigkeitsfelder sind hier vielfältig, die Diskussion darüber steht gerade in Deutschland erst am Anfang.

2022, Anteghini, Marco, D’Souza, Jennifer, dos Santos, Vitor A. P. Martins, Auer, Sören

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. We propose a solution for automatically semantifying biological assays. Our solution contrasts the problem of automated semantification as labeling versus clustering where the two methods are on opposite ends of the method complexity spectrum. Characteristically modeling our problem, we find the clustering solution significantly outperforms a deep neural network state-of-the-art labeling approach. This novel contribution is based on two factors: 1) a learning objective closely modeled after the data outperforms an alternative approach with sophisticated semantic modeling; 2) automatically semantifying biological assays achieves a high performance F1 of nearly 83%, which to our knowledge is the first reported standardized evaluation of the task offering a strong benchmark model.

2018, Sohmen, Lucia, Charbonnier, Jean, Blümel, Ina, Wartena, Christian, Heller, Lambert, Méndez, E., Crestani, F., Ribeiro, C., David, G., Lopes, J.

This paper summarizes the results of a comprehensive statistical analysis on a corpus of open access articles and contained figures. It gives an insight into quantitative relationships between illustrations or types of illustrations, caption lengths, subjects, publishers, author affiliations, article citations and others.

2023, Obrezkov, Denis, Oelen, Allard, Auer, Sören, Abdelnour-Nocera, José L., Marta Lárusdóttir, Petrie, Helen, Piccinno, Antonio, Winckler, Marco

The amount of research articles produced every day is overwhelming: scholarly knowledge is getting harder to communicate and easier to get lost. A possible solution is to represent the information in knowledge graphs: structures representing knowledge in networks of entities, their semantic types, and relationships between them. But this solution has its own drawback: given its very specific task, it requires new methods for designing and evaluating user interfaces. In this paper, we propose an approach for user interface evaluation in the knowledge communication domain. We base our methodology on the well-established Cognitive Walkthough approach but employ a different set of questions, tailoring the method towards domain-specific needs. We demonstrate our approach on a scholarly knowledge graph implementation called Open Research Knowledge Graph (ORKG).

2020, Elias, Mirette, Oelen, Allard, Tavakoli, Mohammadreza, Kismihok, Gábor, Auer, Sören, Alario-Hoyos, Carlos, Rodríguez-Triana, María Jesús, Scheffel, Maren, Arnedillo-Sánchez, Inmaculada, Dennerlein, Sebastian Maximilian

Open Educational Resources (OER) are free and open-licensed educational materials widely used for learning. OER quality assessment has become essential to support learners and teachers in finding high-quality OERs, and to enable online learning repositories to improve their OERs. In this work, we establish a set of evaluation metrics that assess OER quality in OER authoring tools. These metrics provide guidance to OER content authors to create high-quality content. The metrics were implemented and evaluated within SlideWiki, a collaborative OpenCourseWare platform that provides educational materials in presentation slides format. To evaluate the relevance of the metrics, a questionnaire is conducted among OER expert users. The evaluation results indicate that the metrics address relevant quality aspects and can be used to determine the overall OER quality.

2022, Oelen, Allard, Stocker, Markus, Auer, Sören, Aizawa, Akiko

As the number of published scholarly articles grows steadily each year, new methods are needed to organize scholarly knowledge so that it can be more efficiently discovered and used. Natural Language Processing (NLP) techniques are able to autonomously process scholarly articles at scale and to create machine readable representations of the article content. However, autonomous NLP methods are by far not sufficiently accurate to create a high-quality knowledge graph. Yet quality is crucial for the graph to be useful in practice. We present TinyGenius, a methodology to validate NLP-extracted scholarly knowledge statements using microtasks performed with crowdsourcing. The scholarly context in which the crowd workers operate has multiple challenges. The explainability of the employed NLP methods is crucial to provide context in order to support the decision process of crowd workers. We employed TinyGenius to populate a paper-centric knowledge graph, using five distinct NLP methods. In the end, the resulting knowledge graph serves as a digital library for scholarly articles.

2010, Nowak, Manfred, Heller, Lambert, Korzen, Sascha, Bergmann, Julia, Danowski, Patrick

[no abstract available]

2022, Arab Oghli, Omar, D’Souza, Jennifer, Auer, Sören

When semantically describing knowledge graphs (KGs), users have to make a critical choice of a vocabulary (i.e. predicates and resources). The success of KG building is determined by the convergence of shared vocabularies so that meaning can be established. The typical lifecycle for a new KG construction can be defined as follows: nascent phases of graph construction experience terminology divergence, while later phases of graph construction experience terminology convergence and reuse. In this paper, we describe our approach tailoring two AI-based clustering algorithms for recommending predicates (in RDF statements) about resources in the Open Research Knowledge Graph (ORKG) https://orkg.org/. Such a service to recommend existing predicates to semantify new incoming data of scholarly publications is of paramount importance for fostering terminology convergence in the ORKG. Our experiments show very promising results: a high precision with relatively high recall in linear runtime performance. Furthermore, this work offers novel insights into the predicate groups that automatically accrue loosely as generic semantification patterns for semantification of scholarly knowledge spanning 44 research fields.