It therefore takes seriously constraints on a theory of meaning coming from the cognitive structure of human concepts, from the need to learn words, and from the connection between meaning, perception, action, and nonlinguistic thought. The theory treats meanings, like phonological structures, as articulated into substructures or tiers: a division into an algebraic Conceptual Structure and a geometric/ topological Spatial Structure; a division of the former into Propositional Structure and Information Structure; and possibly a division of Propositional Structure into a descriptive tier and a referential tier. All of these structures contribute to word, phrase, and sentence meanings. The ontology of Conceptual Semantics is richer than in most approaches, including not only individuals and events but also locations, trajectories, manners, distances, and other basic categories. Word meanings are decomposed into functions and features, but some of the features and connectives among them do not lend themselves to standard definitions in terms of necessary and sufficient conditions.

Despite the importance of propositional logic in artificial intelligence, the notion of language independence in the propositional setting (not to be confound with syntax independence) has not received much attention so far. In this paper, we define language independence for a propositional operator as robustness w.r.t.symbol translation. We provide a number of characterizations results for such translations. We motivate the need to focus on symbol translations of restricted types, and identify several families of interest. We identify the computational complexity of recognizing symbol translations from those families. Finally, as a case study, we investigate the robustness of belief revision/merging operators w.r.t. translations of different types. It turns out that rational belief revision/merging operators are not guaranteed to offer the most basic (yet non-trivial) form of language independence; operators based on the Hamming distance do not suffer from this drawback but are less robust than operators based on the drastic distance.