@inbook{61150,
  abstract     = {{Since the emergence of the field of eXplainable Artificial Intelligence (XAI), a growing number of researchers have argued that XAI should consider insights from the social sciences in order to adapt explanations to the expectations and needs of human users. This has led to the emergence of a field called Social XAI, which is concerned with understanding how explanations are actively shaped in the interaction between a human user and an AI system. Recognizing this turn in XAI toward making XAI systems more “social” by providing explanations that focus on human information needs and incorporating insights from human–human explanatory interactions, in this paper we provide a formal foundation for Social XAI. We do so by proposing novel ontological accounts of the key terms used in Social XAI based on Basic Formal Ontology (BFO). Specifically, we provide novel ontological accounts for explanandum, explanans, understanding, explanation, explainer, explainee, and context. In doing so, we discuss multifaceted entities in Social XAI (having both continuant and occurrent facets; e.g., explanation) and the relationship between understanding and explanation. Additionally, we propose solutions to seemingly paradoxical views on some terms (e.g., social constructivist vs. individual constructivist perspective on explanandum).}},
  author       = {{Booshehri, Meisam and Buschmeier, Hendrik and Cimiano, Philipp}},
  booktitle    = {{Proceedings of the 15th International Conference on Formal Ontology in Information Systems}},
  isbn         = {{9781643686172}},
  issn         = {{0922-6389}},
  location     = {{Catania, Italy}},
  pages        = {{255–268}},
  publisher    = {{IOS Press}},
  title        = {{{A BFO-based ontological analysis of entities in Social XAI}}},
  doi          = {{10.3233/faia250498}},
  year         = {{2025}},
}

@inbook{57238,
  abstract     = {{<jats:p>Abstract argumentation is a popular toolkit for modeling, evaluating, and comparing arguments. Relationships between arguments are specified in argumentation frameworks (AFs), and conditions are placed on sets (extensions) of arguments that allow AFs to be evaluated. For more expressiveness, AFs are augmented with acceptance conditions on directly interacting arguments or a constraint on the admissible sets of arguments, resulting in dialectic frameworks or constrained argumentation frameworks. In this paper, we consider flexible conditions for rejecting an argument from an extension, which we call rejection conditions (RCs). On the technical level, we associate each argument with a specific logic program. We analyze the resulting complexity, including the structural parameter treewidth. Rejection AFs are highly expressive, giving rise to natural problems on higher levels of the polynomial hierarchy.</jats:p>}},
  author       = {{Fichte, Johannes K. and Hecher, Markus and Mahmood, Yasir and Meier, Arne}},
  booktitle    = {{Frontiers in Artificial Intelligence and Applications}},
  isbn         = {{9781643685489}},
  issn         = {{0922-6389}},
  location     = {{Santiago de Compostela, Spain}},
  publisher    = {{IOS Press}},
  title        = {{{Rejection in Abstract Argumentation: Harder Than Acceptance?}}},
  doi          = {{10.3233/faia240867}},
  year         = {{2024}},
}

@inbook{62702,
  abstract     = {{<jats:p>Clifford algebras are a natural extension of division algebras, including real numbers, complex numbers, quaternions, and octonions. Previous research in knowledge graph embeddings has focused exclusively on Clifford algebras of a specific type, which do not include nilpotent base vectors—elements that square to zero. In this work, we introduce a novel approach by incorporating nilpotent base vectors with a nilpotency index of two, leading to a more general form of Clifford algebras named degenerate Clifford algebras. This generalization to degenerate Clifford algebras does allow for covering dual numbers and as such include translations and rotations models under the same generalization paradigm for the first time. We develop two models to determine the parameters that define the algebra: one using a greedy search and another predicting the parameters based on neural network embeddings of the input knowledge graph. Our evaluation on seven benchmark datasets demonstrates that this incorporation of nilpotent vectors enhances the quality of embeddings. Additionally, our method outperforms state-of-the-art approaches in terms of generalization, particularly regarding the mean reciprocal rank achieved on validation data. Finally, we show that even a simple greedy search can effectively discover optimal or near-optimal parameters for the algebra.</jats:p>}},
  author       = {{Kamdem Teyou, Louis Mozart and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Frontiers in Artificial Intelligence and Applications}},
  isbn         = {{9781643685489}},
  issn         = {{0922-6389}},
  location     = {{Santiago de Compostela}},
  publisher    = {{IOS Press}},
  title        = {{{Embedding Knowledge Graphs in Degenerate Clifford Algebras}}},
  doi          = {{10.3233/faia240627}},
  year         = {{2024}},
}