Auflistung nach Autor:in "Schumacher, Kinga"
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- KonferenzbeitragA paper-based technology for personal knowledge management(WM2009: 5th conference on professional knowledge management, 2009) Schumacher, Kinga; Liwicki, Marcus; Dengel, AndreasIn this paper we extend the Semantic Desktop with a novel input modality: handwriting on paper. This extension allows for annotating printed documents with handwritten notes, which are then recognized, interpreted and integrated into the personal knowledge space. Thus it supports personal knowledge work on paper. In particular, the user can mark any text segment and annotate it with his or her own comments. The semantic of these comments and marks are then interpreted and, finally, the result is included in the user's Semantic Desktop. The methodology for finding the intended meaning of the annotation in context of the marked text is presented in this paper. To demonstrate the applicability of our approach, we have implemented a prototype for the NEPOMUK Semantic Desktop.
- ZeitschriftenartikelSemantic Desktop for the End-UserSemantic Desktop für Anwender(i-com: Vol. 8, No. 3, 2009) Grimnes, Gunnar Aastrand; Adrian, Benjamin; Schwarz, Sven; Maus, Heiko; Schumacher, Kinga; Sauermann, LeoThis article describes the Semantic Desktop. We give insights into the core services that aim to improve personal knowledge management on the desktop. We describe these core components of our Semantic Desktop system and give evaluation results. Results of a long-term study reveal effects of using the Semantic Desktop on personal knowledge work.
- ZeitschriftenartikelThe AI Methods, Capabilities and Criticality Grid(KI - Künstliche Intelligenz: Vol. 35, No. 0, 2021) Schmid, Thomas; Hildesheim, Wolfgang; Holoyad, Taras; Schumacher, KingaMany artificial intelligence (AI) technologies developed over the past decades have reached market maturity and are now being commercially distributed in digital products and services. Therefore, national and international AI standards are currently being developed in order to achieve technical interoperability as well as reliability and transparency. To this end, we propose to classify AI applications in terms of the algorithmic methods used, the capabilities to be achieved and the level of criticality. The resulting three-dimensional classification scheme, termed the AI Methods, Capabilities and Criticality (AI- $$\hbox {MC}^2$$ MC 2 ) Grid, combines current recommendations of the EU Commission with an ethical dimension proposed by the Data Ethics Commission of the German Federal Government (Datenethikkommission der Bundesregierung: Gutachten. Berlin, 2019). As a whole, the AI- $$\hbox {MC}^2$$ MC 2 Grid allows not only to gain an overview of the implications of a given AI application as well as to compare efficiently different AI applications within a given market or implemented by different AI technologies. It is designed as a core tool to define and manage norms, standards and compliance of AI applications, but helps to manage AI solutions in general as well.