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Auflistung nach Schlagwort "Dual-lingual Ontologies"

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  • Konferenzbeitrag
    An analysis of Computer Science in OJAs with a dual-lingual ontology approach
    (INFORMATIK 2024, 2024) Tiemann, Michael; Dörpinghaus, Jens; Shivashankar, Venkatesh Hariharapura
    Increasing globalization of the Internet has led to a growing need for the processing of data in multiple languages. This has resulted in a significant increase in the amount of multilingual data available on the web, presenting a significant challenge for accessing, processing, and integrating data from different language sources. An ontology provides a shared and precise source for system interoperability and the reuse of knowledge bases. The objective of this study is to develop an efficient approach for mapping and enriching cross-domain, dual-lingual ontologies. In this case, we will combine the Computer Science Ontology (CSO) and DBpedia. The resulting taxonomy will be analyzed using a German online job advertisement dataset of 3,567,240 records to identify trends in the development and/or requirement of tools in the Computer Science domain. In order to achieve this, we employ a comparative analysis of two distinct approaches: a holistic, hierarchical approach and a precision-driven approach. The former considers the child-parent relation of the ontology, whereas the latter solely identifies those tools directly associated with a topic in the CSO.
  • Konferenzbeitrag
    Comparing a legacy tools taxonomy with digital tools from Computer Science Ontology
    (INFORMATIK 2024, 2024) Tiemann, Michael; Dörpinghaus, Jens; Hariharapura Shivashankar, Venkatesh; Dorau, Ralf
    In 2018, BIBB introduced a legacy tools taxonomy for labor market research which is enhanced utilizing the Computer Science Ontology (CSO) and dual-language translation to incorporate additional information. The main research question is if it is feasible to enrich or rebuild an existing taxonomy on the basis of CSO. This poster presents a novel approach to enrich the CSO with data from DBpedia, creating a dual-language ontology enriched with tools and entities. This includes an ML-based approach to identify tools and compares the results with the legacy work tools taxonomy to identify the gaps.