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End date: 2024 × Start date: 2020 × End date: 2019 × Start date: 2011 × WGL Institute: TIB ×

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    EVENTSKG: A 5-Star Dataset of Top-Ranked Events in Eight Computer Science Communities
    (Berlin ; Heidelberg : Springer, 2019) Fathalla, Said; Lange, Christoph; Auer, Sören; Hitzler, Pascal; Fernández, Miriam; Janowicz, Krzysztof; Zaveri, Amrapali; Gray, Alasdair J.G.; Lopez, Vanessa; Haller, Armin; Hammar, Karl
    Metadata of scientific events has become increasingly available on the Web, albeit often as raw data in various formats, disregarding its semantics and interlinking relations. This leads to restricting the usability of this data for, e.g., subsequent analyses and reasoning. Therefore, there is a pressing need to represent this data in a semantic representation, i.e., Linked Data. We present the new release of the EVENTSKG dataset, comprising comprehensive semantic descriptions of scientific events of eight computer science communities. Currently, EVENTSKG is a 5-star dataset containing metadata of 73 top-ranked event series (almost 2,000 events) established over the last five decades. The new release is a Linked Open Dataset adhering to an updated version of the Scientific Events Ontology, a reference ontology for event metadata representation, leading to richer and cleaner data. To facilitate the maintenance of EVENTSKG and to ensure its sustainability, EVENTSKG is coupled with a Java API that enables users to add/update events metadata without going into the details of the representation of the dataset. We shed light on events characteristics by analyzing EVENTSKG data, which provides a flexible means for customization in order to better understand the characteristics of renowned CS events.
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    Die Rolle der ORCID iD in der Wissenschaftskommunikation: Der Beitrag des ORCID-Deutschland-Konsortiums und das ORCID-DE-Projekt
    (Berlin : de Gruyter Saur, 2019) Dreyer, Britta; Hagemann-Wilholt, Stephanie; Vierkant, Paul; Strecker, Dorothea; Glagla-Dietz, Stephanie; Summann, Friedrich; Pampel, Heinz; Burger, Marleen
    ORCID’s services such as the unambiguous linking of researchers and their research output form the basis of modern scholarly communication. The ORCID Germany Consortium offers a reduced ORCID premium membership fee and supports its members during ORCID integration. Services include a dialogue platform that provides German-language information and additional support services. Another major success factor is an all-encompassing communication strategy: members of the ORCID implementation can resort to established organizational communication channels. Together and with the support of the ORCID DE project they contribute significantly to the successful distribution of ORCID in Germany.
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    The Research Core Dataset (KDSF) in the Linked Data context
    (Amsterdam [u.a.] : Elsevier, 2019) Walther, Tatiana; Hauschke, Christian; Kasprzik, Anna; Sicilia, Miguel-Angel; Simons, Ed; Clements, Anna; de Castro, Pablo; Bergström, Johan
    This paper describes our efforts to implement the Research Core Dataset (“Kerndatensatz Forschung”; KDSF) as an ontology in VIVO. KDSF is used in VIVO to record the required metadata on incoming data and to produce reports as an output. While both processes need an elaborate adaptation of the KDSF specification, this paper focusses on the adaptation of the KDSF basic data model for recording data in VIVO. In this context, the VIVO and KDSF ontologies were compared with respect to domain, syntax, structure, and granularity in order to identify correspondences and mismatches. To produce an alignment, different matching approaches have been applied. Furthermore, we made necessary modifications and extensions on KDSF classes and properties.
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    Figures in Scientific Open Access Publications
    (New York, NY : Springer, 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.
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    SemSur: A Core Ontology for the Semantic Representation of Research Findings
    (Amsterdam [u.a.] : Elsevier, 2018) Fathalla, Said; Vahdati, Sahar; Auer, Sören; Lange, Christoph; Fensel, Anna; de Boer, Victor; Pellegrini, Tassilo; Kiesling, Elmar; Haslhofer, Bernhard; Hollink, Laura; Schindler, Alexander
    The way how research is communicated using text publications has not changed much over the past decades. We have the vision that ultimately researchers will work on a common structured knowledge base comprising comprehensive semantic and machine-comprehensible descriptions of their research, thus making research contributions more transparent and comparable. We present the SemSur ontology for semantically capturing the information commonly found in survey and review articles. SemSur is able to represent scientific results and to publish them in a comprehensive knowledge graph, which provides an efficient overview of a research field, and to compare research findings with related works in a structured way, thus saving researchers a significant amount of time and effort. The new release of SemSur covers more domains, defines better alignment with external ontologies and rules for eliciting implicit knowledge. We discuss possible applications and present an evaluation of our approach with the retrospective, exemplary semantification of a survey. We demonstrate the utility of the SemSur ontology to answer queries about the different research contributions covered by the survey. SemSur is currently used and maintained at OpenResearch.org.
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    Interaction Network Analysis Using Semantic Similarity Based on Translation Embeddings
    (Berlin ; Heidelberg : Springer, 2019) Manzoor Bajwa, Awais; Collarana, Diego; Vidal, Maria-Esther; Acosta, Maribel; Cudré-Mauroux, Philippe; Maleshkova, Maria; Pellegrini, Tassilo; Sack, Harald; Sure-Vetter, York
    Biomedical knowledge graphs such as STITCH, SIDER, and Drugbank provide the basis for the discovery of associations between biomedical entities, e.g., interactions between drugs and targets. Link prediction is a paramount task and represents a building block for supporting knowledge discovery. Although several approaches have been proposed for effectively predicting links, the role of semantics has not been studied in depth. In this work, we tackle the problem of discovering interactions between drugs and targets, and propose SimTransE, a machine learning-based approach that solves this problem effectively. SimTransE relies on translating embeddings to model drug-target interactions and values of similarity across them. Grounded on the vectorial representation of drug-target interactions, SimTransE is able to discover novel drug-target interactions. We empirically study SimTransE using state-of-the-art benchmarks and approaches. Experimental results suggest that SimTransE is competitive with the state of the art, representing, thus, an effective alternative for knowledge discovery in the biomedical domain.
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    Preface
    (Aachen, Germany : RWTH Aachen, 2019) Kaffee, Lucie-Aimee; Endris, Kemele M.; Vidal, Maria-Esther; Comerio, Marco; Sadeghi, Mersedeh; Chaves-Fraga; David, Colpaert Pieter; Kaffee, Lucie Aimée; Endris, Kemele M.; Vidal, María-Esther; Comerio, Marco; Sadeghi, Mersedeh; Chaves-Fraga, David; Colpaert, Pieter
    This volumne presents the proceedings of the 1st International Workshop on Approaches for Making Data Interoperable (AMAR 2019) and 1st International Workshop on Semantics for Transport (Sem4Tra) held in Karlsruhe, Germany, September 9, 2019, co-located with SEMANTiCS 2019. Interoperability of data is an important factor to make transportation data accessible, therefore we present the topics alongside each other in this proceedings.
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    Curating Scientific Information in Knowledge Infrastructures
    (Paris : CODATA, 2018) Stocker, Markus; Paasonen, Pauli; Fiebig, Markus; Zaidan, Martha A.; Hardisty, Alex
    Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures. Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations. The result of interpretation is information—meaningful secondary or derived data—about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps. In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation—as machine readable data and their machine readable meaning—is not common practice among environmental research infrastructures. For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon.
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    Open Science und die Bibliothek – Aktionsfelder und Berufsbild
    (Graz : Vereinigung Österreichischer Bibliothekarinnen und Bibliothekare, 2019) Blümel, Ina; Drees, Bastian; Hauschke, Christian; Heller, Lambert; Tullney, Marco
    Eine durch die Digitalisierung veränderte und auf Open Science ausgerichtete Wissenschaftspraxis benötigt angepasste Infrastrukturen und Services. Daraus ergeben sich verschiedene neue oder veränderte Aktionsfelder für wissenschaftliche Bibliotheken und Infrastruktureinrichtungen. Zu nennen sind zum Beispiel die nicht-textuellen Materialien wie Forschungsdaten, AV-Medien oder Software und die Umsetzung der FAIR-Prinzipien. Hinzu kommen neue Aufgaben im Bereich der Forschungsinformationen, zum Beispiel in der Unterstützung institutioneller Forschungsinformationssysteme, die Gestaltung von Open Access, die Unterstützung kollaborativen wissenschaftlichen Arbeitens sowie die Schaffung von offenen Infrastrukturen. In diesem Artikel werden diese Felder kurz vorgestellt und sich daraus abzeichnende Anforderungen an das bibliothekarische Berufsbild skizziert.
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    “When was this picture taken?” – Image date estimation in the wild
    (Berlin : Springer Verlag, 2017) Müller, E.; Springstein, M.; Ewerth, R.
    The problem of automatically estimating the creation date of photos has been addressed rarely in the past. In this paper, we introduce a novel dataset Date Estimation in the Wild for the task of predicting the acquisition year of images captured in the period from 1930 to 1999. In contrast to previous work, the dataset is neither restricted to color photography nor to specific visual concepts. The dataset consists of more than one million images crawled from Flickr and contains a large number of different motives. In addition, we propose two baseline approaches for regression and classification, respectively, relying on state-of-the-art deep convolutional neural networks. Experimental results demonstrate that these baselines are already superior to annotations of untrained humans.