Non-monotonic Extensions to Formal Concept Analysis via Object Preferences
<|reference_start|>Non-monotonic Extensions to Formal Concept Analysis via Object Preferences: Formal Concept Analysis (FCA) is an approach to creating a conceptual hierarchy in which a \textit{concept lattice} is generated from a \textit{formal context}. That is, a triple consisting of a set of objects, $G$, a set of attributes, $M$, and an incidence relation $I$ on $G \times M$. A \textit{concept} is then modelled as a pair consisting of a set of objects (the \textit{extent}), and a set of shared attributes (the \textit{intent}). Implications in FCA describe how one set of attributes follows from another. The semantics of these implications closely resemble that of logical consequence in classical logic. In that sense, it describes a monotonic conditional. The contributions of this paper are two-fold. First, we introduce a non-monotonic conditional between sets of attributes, which assumes a preference over the set of objects. We show that this conditional gives rise to a consequence relation that is consistent with the postulates for non-monotonicty proposed by Kraus, Lehmann, and Magidor (commonly referred to as the KLM postulates). We argue that our contribution establishes a strong characterisation of non-monotonicity in FCA. Typical concepts represent concepts where the intent aligns with expectations from the extent, allowing for an exception-tolerant view of concepts. To this end, we show that the set of all typical concepts is a meet semi-lattice of the original concept lattice. This notion of typical concepts is a further introduction of KLM-style typicality into FCA, and is foundational towards developing an algebraic structure representing a concept lattice of prototypical concepts.<|reference_end|>
@article{carr2024non-monotonic,
    title={Non-monotonic Extensions to Formal Concept Analysis via Object
  Preferences},
    author={Lucas Carr, Nicholas Leisegang, Thomas Meyer, Sebastian Rudolph},
    journal={arXiv preprint arXiv:2410.04184},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04184},
    primaryClass={cs.LO cs.AI}
}