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Auflistung nach Schlagwort "Knowledge Representation"

1 - 3 von 3
  • Konferenzbeitrag
    Ensuring trustworthy AI for sensitive infrastructure using Knowledge Representation
    (INFORMATIK 2024, 2024) Mejri, Oumayma; Waedt, Karl; Yatagha, Romarick; Edeh, Natasha; Sebastiao, Claudia Lemos
    Artificial intelligence (AI) has become increasingly integrated into various aspects of society, from healthcare and finance to law enforcement and hiring processes. More recently, sensitive infrastructure such as nuclear plants is engaging AI in aspects of safety. However, these systems are not immune to biases and ethical concerns. This paper explores the role of knowledge representation in addressing ethics and fairness in AI, examining how biased or incomplete representations can lead to unfair outcomes and unreliable decision-making. It proposes strategies to mitigate these risks.
  • Workshopbeitrag
    Semiotic Models: Advancing HCI through Simulating Consciousness
    (Mensch und Computer 2024 - Workshopband, 2024) Wernsdorfer, Mark
    This position paper proposes a novel graph-based model of consciousness with significant implications for Human-Computer Interaction (HCI) and its implementation as a new kind of AI system: \emph{Semiotic models.} Drawing inspiration from European phenomenology, Peircean semiotics, and Searlean philosophy of mind, we posit that subjective experience emerges primarily from a system's unique perspective, forming the foundation for a hierarchical graph structure that represents conscious concepts. Semiotic models not only address the symbol grounding problem in AI but also open new avenues for designing more intuitive and responsive human-AI interfaces. We present a computational implementation of semiotic models, draft options for evaluating their performance in HCI-relevant tasks, and discuss their potential for user-centered AI design.
  • Textdokument
    Using Knowledge Graphs to Manage a Data Lake
    (INFORMATIK 2020, 2021) Dibowski, Henrik; Schmid, Stefan
    Knowledge graphs as fundamental pillar of artificial intelligence are experiencing a strong demand. In contrast to machine learning and deep learning, knowledge graphs do not require large amounts of (training) data and offer a bigger potential for a multitude of domains and problems. This article shows the application of knowledge graphs for the semantic description and management of data in a data lake, which improves the findability and reusability of data, and enables the automatic processing by algorithms. Since knowledge graphs contain both the data as well as its semantically described schema (ontology), they enable novel ontology-driven software architectures, in which the domain knowledge and business logic can completely reside on the knowledge graph level. This article further introduces such a use case: an ontology-driven frontend implementation, which is able to fully adapt itself based on the underlying knowledge graph schema and dynamically render information in the desired manner.