In this paper, we present a fuzzy logic-based extension of such semantic BIMs that provides support for imprecise knowledge representation and retrieval.
It is widely thought that humans possess imprecise internal representations of magnitudes (henceforth referred to as magnitude representations) that correspond to symbolic numbers in the external environment (Domahs et al., 2012; Lyons & Ansari, 2008; Verguts & Fias, 2004; Young & Opfer, 2011; Zhang & Wang, 2005).
Since adjacent vertices in a de Bruijn graph share overlapping sequence fragments of length k − 1 according to the definition of a de Bruijn graph, the location of junctions is imprecise and this representation is hard to interpret.
Other examples are monotone measures, such as Möbius representations, imprecise probabilities and decision weights, as well as Shannon entropy.
Results suggest that interindividual congruency of mental representations of imprecise terms on frequency or intensity generally appears to be low.
Generally, the results indicate that mental representations of imprecise terms on frequency or intensity can differ depending on patient characteristics (e.g., age, gender, mental symptom burden, lexical experience).
Errors arising from imprecise conversion of knowledge into logical representation.
It is argued that the estimates are often imprecise due to several problems regarding the representation of migrants in survey data.
Being formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research.
Fuzzy Description Logics (DLs) are a family of knowledge representation formalisms designed to represent and reason about vague and imprecise knowledge that is inherent to many application domains.
