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Auflistung nach Autor:in "Schaible, Johann"

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  • Zeitschriftenartikel
    Applying Linked Data Technologies in the Social Sciences
    (KI - Künstliche Intelligenz: Vol. 30, No. 2, 2016) Zapilko, Benjamin; Schaible, Johann; Wandhöfer, Timo; Mutschke, Peter
    In recent years, Linked Open Data (LOD) has matured and gained acceptance across various communities and domains. Large potential of Linked Data technologies is seen for an application in scientific disciplines. In this article, we present use cases and applications for an application of Linked Data in the social sciences. They focus on (a) interlinking domain-specific information, and (b) linking social science data to external LOD sources (e.g. authority data) from other domains. However, several technical and research challenges arise, when applying Linked Data technologies to a scientific domain with its specific data, information needs and use cases. We discuss these challenges and show how they can be addressed.
  • Zeitschriftenartikel
    Evaluation Infrastructures for Academic Shared Tasks
    (Datenbank-Spektrum: Vol. 20, No. 1, 2020) Schaible, Johann; Breuer, Timo; Tavakolpoursaleh, Narges; Müller, Bernd; Wolff, Benjamin; Schaer, Philipp
    Academic search systems aid users in finding information covering specific topics of scientific interest and have evolved from early catalog-based library systems to modern web-scale systems. However, evaluating the performance of the underlying retrieval approaches remains a challenge. An increasing amount of requirements for producing accurate retrieval results have to be considered, e.g., close integration of the system’s users. Due to these requirements, small to mid-size academic search systems cannot evaluate their retrieval system in-house. Evaluation infrastructures for shared tasks alleviate this situation. They allow researchers to experiment with retrieval approaches in specific search and recommendation scenarios without building their own infrastructure. In this paper, we elaborate on the benefits and shortcomings of four state-of-the-art evaluation infrastructures on search and recommendation tasks concerning the following requirements: support for online and offline evaluations, domain specificity of shared tasks, and reproducibility of experiments and results. In addition, we introduce an evaluation infrastructure concept design aiming at reducing the shortcomings in shared tasks for search and recommender systems.
  • Konferenzbeitrag
    A survey to identify factors for vocabulary reuse and requirements for vocabulary recommendation tools
    (INFORMATIK 2013 – Informatik angepasst an Mensch, Organisation und Umwelt, 2013) Schaible, Johann
    The choice of appropriate vocabularies is one essential aspect in the guidelines that guide a data engineer when modeling Linked Open Data (LOD). In general, this leads to an easier consumption of the data by LOD applications and users. However, making decisions considering the adequacy of various vocabularies is not straightforward and a well known challenge; the same applies to the engineer's decisionmaking regarding the total number of vocabularies used in one dataset. Therefore, it is not surprising that according to some LOD data provider studies, there is still an insufficient compliance towards this particular best practice. In this paper, we examine the current importance of the best practice “vocabulary reuse”, as well as the factors that influence the engineer's decision whether to reuse a specific vocabulary or not. We provide results of an online survey comprising an aggregation of knowledge, practices, and design motivations of several LOD publishers and practitioners with respect to the reuse of vocabularies. These results show that the insufficient compliance considering vocabulary reuse is not because of its lack of importance, but most likely because of deficient tool support for deciding which and how many vocabularies to reuse. We address the increased need for such tool support, and based on the results of the study, we derive several requirements for future vocabulary recommendation tools.