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.

2021, Inderst, Rudolf, Heller, Lambert

Rudolf Inderst und Lambert Heller stellen die grundsätzliche Frage, ob Text überhaupt die richtige Form ist, um sich mit digitalen Spielen wissenschaftlich auseinanderzusetzen. Sie sprechen sich dabei für die Etablierung und Verwendung der Form des Videoessays ein, die bereits in ihrer audiovisuellen Materialität dem Gegenstand angemessener sei.

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, 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.

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

2023, D’Souza, Jennifer, Mulang, Isaiah Onando, Auer, Sören

In multiple-choice exams, students select one answer from among typically four choices and can explain why they made that particular choice. Students are good at understanding natural language questions and based on their domain knowledge can easily infer the question's answer by “connecting the dots” across various pertinent facts. Considering automated reasoning for elementary science question answering, we address the novel task of generating explanations for answers from human-authored facts. For this, we examine the practically scalable framework of feature-rich support vector machines leveraging domain-targeted, hand-crafted features. Explanations are created from a human-annotated set of nearly 5000 candidate facts in the WorldTree corpus. Our aim is to obtain better matches for valid facts of an explanation for the correct answer of a question over the available fact candidates. To this end, our features offer a comprehensive linguistic and semantic unification paradigm. The machine learning problem is the preference ordering of facts, for which we test pointwise regression versus pairwise learning-to-rank. Our contributions, originating from comprehensive evaluations against nine existing systems, are (1) a case study in which two preference ordering approaches are systematically compared, and where the pointwise approach is shown to outperform the pairwise approach, thus adding to the existing survey of observations on this topic; (2) since our system outperforms a highly-effective TF-IDF-based IR technique by 3.5 and 4.9 points on the development and test sets, respectively, it demonstrates some of the further task improvement possibilities (e.g., in terms of an efficient learning algorithm, semantic features) on this task; (3) it is a practically competent approach that can outperform some variants of BERT-based reranking models; and (4) the human-engineered features make it an interpretable machine learning model for the task.

2023, Stocker, Markus, Rossenova, Lozana, Shigapov, Renat, Betancort, Noemi, Dietze, Stefan, Murphy, Bridget, Bölling, Christian, Schubotz, Moritz, Koepler, Oliver

Knowledge Graphs are a key technology for implementing the FAIR principles in data infrastructures by ensuring interoperability for both humans and machines. The Working Group "Knowledge Graphs" in Section "(Meta)data, Terminologies, Provenance" of the German National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur (NFDI) e.V.) aims to promote the use of knowledge graphs in all NFDI consortia, to facilitate cross-domain data interlinking and federation following the FAIR principles, and to contribute to the joint development of tools and technologies that enable transformation of structured and unstructured data into semantically reusable knowledge across different domains.

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).

2023, Cheema, Gullal S., Hakimov, Sherzod, Müller-Budack, Eric, Otto, Christian, Bateman, John A., Ewerth, Ralph

The analysis of news dissemination is of utmost importance since the credibility of information and the identification of disinformation and misinformation affect society as a whole. Given the large amounts of news data published daily on the Web, the empirical analysis of news with regard to research questions and the detection of problematic news content on the Web require computational methods that work at scale. Today's online news are typically disseminated in a multimodal form, including various presentation modalities such as text, image, audio, and video. Recent developments in multimodal machine learning now make it possible to capture basic “descriptive” relations between modalities–such as correspondences between words and phrases, on the one hand, and corresponding visual depictions of the verbally expressed information on the other. Although such advances have enabled tremendous progress in tasks like image captioning, text-to-image generation and visual question answering, in domains such as news dissemination, there is a need to go further. In this paper, we introduce a novel framework for the computational analysis of multimodal news. We motivate a set of more complex image-text relations as well as multimodal news values based on real examples of news reports and consider their realization by computational approaches. To this end, we provide (a) an overview of existing literature from semiotics where detailed proposals have been made for taxonomies covering diverse image-text relations generalisable to any domain; (b) an overview of computational work that derives models of image-text relations from data; and (c) an overview of a particular class of news-centric attributes developed in journalism studies called news values. The result is a novel framework for multimodal news analysis that closes existing gaps in previous work while maintaining and combining the strengths of those accounts. We assess and discuss the elements of the framework with real-world examples and use cases, setting out research directions at the intersection of multimodal learning, multimodal analytics and computational social sciences that can benefit from our approach.

2023-04-24, Kaliuzhna, Nataliia

The purpose of the article is to conduct a comprehensive, objective and critical analysis of the results of research on open access in Ukraine to assess the current state of the topic and identify the aspects which require deeper study in order to develop an effective mechanism for implementing the principles of open access in practice. Research methods. The method of narrative literature review with a defined search strategy and criteria for selecting publications in four databases, such as Dimensions, Scopus, Web of Science and the depository of electronic copies “Scientific Periodicals of Ukraine” was used. The inclusion criteria were articles published by authors affiliated with Ukrainian institutions. The scientific novelty lies in the fact that the literature analysis helped to expand and deepen knowledge about the thematic areas of open access research in Ukraine; to identify previously unknown links and contradictions between studies; and to identify areas that require further studies, which include, in particular, the analysis of factors and barriers that facilitate or prevent Ukrainian authors from disseminating their works in open access, the dynamics of growth and peculiarities of the distribution of the share of open access publications by year and field of study. Conclusions. Open access is one of the main components of Open Science. It serves as a tool for accelerating knowledge sharing, developing science, helping to eliminate inequality, and helping to solve several global problems. The research of Ukrainian scientists focuses on four primary areas of open access research: the development of open access policies at the level of institutions and the state, the introduction of institutional repositories, the launch of open access journals, and the regulation of copyright in open scientific communication.