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- ItemBridging the Gap Between (AI-) Services and Their Application in Research and Clinical Settings Through Interoperability: the OMI-Protocol(Hannover : Technische Informationsbibliothek, 2024-02) Sigle, Stefan; Werner, Patrick; Schweizer, Simon; Caldeira, Liliana; Hosch, René; Dyrba, Martin; Fegeler, Christian; Sigle, Stefan; Werner, Patrick; Schweizer, Simon; Caldeira, Liliana; Hosch, René; Dyrba, Martin; Fegeler, Christian; Grönke, Ana; Seletkov, Dmitrii; Kotter, Elmar; Nensa, Felix; Wehrle, Julius; Kaufmes, Kevin; Scherer, Lucas; Nolden, Marco; Boeker, Martin; Schmidt, Marvin; Pelka, Obioma; Braren, Rickmer; Stump, Shura-Roman; Graetz, Teresa; Pogarell, Tobias; Susetzky, Tobias; Wieland, Tobias; Parmar, Vicky; Wang, YuanbinArtificial Intelligence (AI) in research and clinical contexts is transforming the areas of medical and life sciences permanently. Aspects like findability, accessibility, interoperability, and reusability are often neglected for AI-based inference services. The Open Medical Inference (OMI) protocol aims to support remote inference by addressing the aforementioned aspects. Key component of the proposed protocol is an interoperable registry for remote inference services, which addresses the issue of findability for algorithms. It is complemented by information on how to invoke services remotely. Together, these components lay the basis for the implementation of distributed inference services beyond organizational borders. The OMI protocol considers prior work for aspects like data representation and transmission standards wherever possible. Based on Business Process Modeling of prototypical use cases for the service registry and common inference processes, a generic information model for remote services was inferred. Based on this model, FHIR resources were identified to represent AI-based services. The OMI protocol is first introduced using AI-services in radiology but is designed to be generalizable to other application domains as well. It provides an accessible, open specification as blueprint for the introduction and implementation of remote inference services.
- ItemAnalysis of Knowledge Tracing performance on synthesised student data(Hannover : Technische Informationsbibliothek, 2024) Pagonis, Panagiotis; Hartung, Kai; Wu, Di; Georges, Munir; Gröttrup, SörenKnowledge Tracing (KT) aims to predict the future performance of students by tracking the development of their knowledge states. Despite all the recent progress made in this field, the application of KT models in education systems is still restricted from the data perspectives: 1) limited access to real life data due to data protection concerns, 2) lack of diversity in public datasets, 3) noises in benchmark datasets such as duplicate records. To resolve these problems, we simulated student data with three statistical strategies based on public datasets and tested their performance on two KT baselines. While we observe only minor performance improvement with additional synthetic data, our work shows that using only synthetic data for training can lead to similar performance as real data.
- ItemOpen Research Knowledge Graph(Goettingen: Cuvillier Verlag, 2024-05-07) Auer, Sören; Ilangovan, Vinodh; Stocker, Markus; Tiwari, Sanju; Vogt, Lars; Bernard-Verdier, Maud; D'Souza, Jennifer; Fadel , Kamel; Farfar, Kheir Eddine; Göpfert , Jan; Haris , Muhammad; Heger, Tina; Hussein, Hassan; Jaradeh, Yaser; Jeschke, Jonathan M.; Jiomekong , Azanzi; Kabongo, Salomon; Karras, Oliver; Kuckertz, Patrick; Kullamann, Felix; Martin, Emily A.; Oelen, Allard; Perez-Alvarez, Ricardo; Prinz, Manuel; Snyder, Lauren D.; Stolten, Detlef; Weinand, Jann M.As we mark the fifth anniversary of the alpha release of the Open Research Knowledge Graph (ORKG), it is both timely and exhilarating to celebrate the significant strides made in this pioneering project. We designed this book as a tribute to the evolution and achievements of the ORKG and as a practical guide encapsulating its essence in a form that resonates with both the general reader and the specialist. The ORKG has opened a new era in the way scholarly knowledge is curated, managed, and disseminated. By transforming vast arrays of unstructured narrative text into structured, machine-processable knowledge, the ORKG has emerged as an essential service with sophisticated functionalities. Over the past five years, our team has developed the ORKG into a vibrant platform that enhances the accessibility and visibility of scientific research. This book serves as a non-technical guide and a comprehensive reference for new and existing users that outlines the ORKG’s approach, technologies, and its role in revolutionizing scholarly communication. By elucidating how the ORKG facilitates the collection, enhancement, and sharing of knowledge, we invite readers to appreciate the value and potential of this groundbreaking digital tool presented in a tangible form. Looking ahead, we are thrilled to announce the upcoming unveiling of promising new features and tools at the fifth-year celebration of the ORKG’s alpha release. These innovations are set to redefine the boundaries of machine assistance enabled by research knowledge graphs. Among these enhancements, you can expect more intuitive interfaces that simplify the user experience, and enhanced machine learning models that improve the automation and accuracy of data curation. We also included a glossary tailored to clarifying key terms and concepts associated with the ORKG to ensure that all readers, regardless of their technical background, can fully engage with and understand the content presented. This book transcends the boundaries of a typical technical report. We crafted this as an inspiration for future applications, a testament to the ongoing evolution in scholarly communication that invites further collaboration and innovation. Let this book serve as both your guide and invitation to explore the ORKG as it continues to grow and shape the landscape of scientific inquiry and communication.