Filters

2017 - 201912020 - 20245

More filters

2019 - 201912020 - 20245
Reset filters

Settings

search.filters.applied.f.access: openAccess × Subject: ontology ×

Search Results

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    Toward a Li-Ion Battery Ontology Covering Production and Material Structure
    (Weinheim [u.a.] : Wiley-VCH, 2022) Mutz, Marcel; Perovic, Milena; Gümbel, Philip; Steinbauer, Veit; Taranovskyy, Andriy; Li, Yunjie; Beran, Lisa; Käfer, Tobias; Dröder, Klaus; Knoblauch, Volker; Kwade, Arno; Presser, Volker; Werth, Dirk; Kraus, Tobias
    An ontology for the structured storage, retrieval, and analysis of data on lithium-ion battery materials and electrode-to-cell production is presented. It provides a logical structure that is mapped onto a digital architecture and used to visualize, correlate, and make predictions in battery production, research, and development. Materials and processes are specified using a predetermined terminology; a chain of unit processes (steps) connects raw materials and products (items) of battery cell production. The ontology enables the attachment of analytical methods (characterization methods) to items. Workshops and interviews with experts in battery materials and production processes are conducted to ensure that the structure is conformable both for industrial-scale and laboratory-scale data generation and implementation. Raw materials and intermediate products are identified and defined for all steps to the final battery cell. Steps and items are defined based on current standard materials and process chains using terms that are in common use. Alternative structures and the connection of the ontology to other existing ontologies are discussed. The contribution provides a pragmatic, accessible way to unify the storage of materials-oriented lithium-ion battery production data. It aids the linkage of such data with domain knowledge and the automation of data analysis in production and research.
  • Thumbnail Image
    Item
    Tagungsbericht VIVO-Workshop 2017 - “Forschungsinformationen in der Praxis”
    (Luzern : Verein Informationspraxis, 2017) Mersmann, Jana; Hauschke, Christian
    Der Wissens- und Erfahrungsaustausch stand im Fokus des 2.VIVO-Workshop 2017 an der Technischen Informationsbibliothek (TIB) in Hannover. Der Workshop, der von rund 40 Teilnehmer/innen aus deutschsprachigen Hochschulen und Universitäten besucht wurde, war in zwei verschiedene Session unterteilt. In vorangestellten Vorträgen wurden sowohl technische Anpassungen und Weiterentwicklungen am Forschungsinformationssystem VIVO an einzelnen Einrichtungen erläutert, als auch Erfahrungsberichte einzelner Anwender/innen geteilt sowie Einsatzmöglichkeiten von VIVO in verschiedenen Kontexten thematisiert. Im anschließenden interaktiven Teil wurden Bedarfe und Herausforderungen diskutiert und gesammelt, die anschließend priorisiert wurden. Als Herausforderungen für die VIVO-Entwickler kristallisierten sich Verbesserungen in den Bereichen Reporting, Datenintegration und einem in den Administrationsbereich integrierten Rollenmanagement heraus. Der Workshop adressierte sehr erfolgreich den ebenso vielfach geäußerten Wunsch nach Vernetzung, Austausch und Fortbildung innerhalb der VIVO-Community und darüber hinaus.
  • Thumbnail Image
    Item
    Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis †
    (Basel : MDPI, 2023) Korel, Lukáš; Yorsh, Uladzislau; Behr, Alexander S.; Kockmann, Norbert; Holeňa, Martin
    The paper presents a machine-learning based approach to text-to-ontology mapping. We explore a possibility of matching texts to the relevant ontologies using a combination of artificial neural networks and classifiers. Ontologies are formal specifications of the shared conceptualizations of application domains. While describing the same domain, different ontologies might be created by different domain experts. To enhance the reasoning and data handling of concepts in scientific papers, finding the best fitting ontology regarding description of the concepts contained in a text corpus. The approach presented in this work attempts to solve this by selection of a representative text paragraph from a set of scientific papers, which are used as data set. Then, using a pre-trained and fine-tuned Transformer, the paragraph is embedded into a vector space. Finally, the embedded vector becomes classified with respect to its relevance regarding a selected target ontology. To construct representative embeddings, we experiment with different training pipelines for natural language processing models. Those embeddings in turn are later used in the task of matching text to ontology. Finally, the result is assessed by compressing and visualizing the latent space and exploring the mappings between text fragments from a database and the set of chosen ontologies. To confirm the differences in behavior of the proposed ontology mapper models, we test five statistical hypotheses about their relative performance on ontology classification. To categorize the output from the Transformer, different classifiers are considered. These classifiers are, in detail, the Support Vector Machine (SVM), k-Nearest Neighbor, Gaussian Process, Random Forest, and Multilayer Perceptron. Application of these classifiers in a domain of scientific texts concerning catalysis research and respective ontologies, the suitability of the classifiers is evaluated, where the best result was achieved by the SVM classifier.
  • Thumbnail Image
    Item
    Ontologies4Chem: The landscape of ontologies in chemistry
    (Berlin : de Gruyter, 2022) Strömert, Philip; Hunold, Johannes; Castro, André; Neumann, Steffen; Koepler, Oliver
    For a long time, databases such as CAS, Reaxys, PubChem or ChemSpider mostly rely on unique numerical identifiers or chemical structure identifiers like InChI, SMILES or others to link data across heterogeneous data sources. The retrospective processing of information and fragmented data from text publications to maintain these databases is a cumbersome process. Ontologies are a holistic approach to semantically describe data, information and knowledge of a domain. They provide terms, relations and logic to semantically annotate and link data building knowledge graphs. The application of standard taxonomies and vocabularies from the very beginning of data generation and along research workflows in electronic lab notebooks (ELNs), software tools, and their final publication in data repositories create FAIR data straightforwardly. Thus a proper semantic description of an investigation and the why, how, where, when, and by whom data was produced in conjunction with the description and representation of research data is a natural outcome in contrast to the retrospective processing of research publications as we know it. In this work we provide an overview of ontologies in chemistry suitable to represent concepts of research and research data. These ontologies are evaluated against several criteria derived from the FAIR data principles and their possible application in the digitisation of research data management workflows.
  • Thumbnail Image
    Item
    AEON - Die Academic Event Ontology
    (Zenodo, 2021) Strömert, Philip
    Die Academic Event Ontology (AEON) ist eines der ersten Module einer zukünftigen erneuerten und modularisierten VIVO-Ontologie 2.0. Mit Hilfe von AEON sollen die relevanten Metadaten von wissenschaftlichen Veranstaltungen und Veranstaltungsreihen, inklusive der verschiedenen Rollen aller Beteiligten, maschinenlesbarer und besser teilbar werden. Die Entwicklung von AEON ist einerseits eine Antwort auf die Anforderung an moderne FIS-Systeme. Ebenso ist sie ein erster Schritt in die richtige Richtung, um bessere Aussagen über die Qualität wissenschaftlichen Veranstaltungen und Veranstaltungsreihen machen zu können. In diesem Vortrag wird ein Überblick über die Ontologie gegeben und es werden die noch bevorstehenden Herausforderungen aufgezeigt. Dabei werden sowohl die Wahl der BFO als "Upper Ontology" als auch die benötigten Anknüpfungspunkte zu anderen bestehenden OBO und sich noch in der Entwicklung befindenenden VIVO-Ontologie-Modulen kurz thematisiert.
  • Thumbnail Image
    Item
    Open 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.