Quantification is common in text, but it is underrepresented in AMR. Quantification also stresses the common conjunctive interpretation of AMR graphs, since universal quantification introduces scope-taking structure and variable binding that cannot be captured as a flat list of conjuncts. We propose an enriched AMR that supports quantificational meaning while keeping AMR’s graph backbone. At the predicate level, we add QuantML features, such as domain restriction, determinacy, distributivity, and involvement. At the discourse level, we add contextual constraints that encode scope and other discourse-sensitive conditions. The two levels follow the UMR architecture and are linked by shared identifiers. We map the enriched graphs to two-block logical forms: a minimal model of events and participants, plus a constraint block that relates them. QuantML, quantification, predicate level, discourse level, discourse constraint, minimal model

This paper explores the meaning of quantification in Korean and how it is encoded in Abstract Meaning Representation (AMRg:2019) and an enriched version AMR+ accommodating Uniform Meaning Representation (UMRg:2022) and some of the contextual constraints proposed by Bos(2020). The extension makes five special references: Bunt et al. (2018), Bunt and Lee (2025), Pustejovsky et al. (2019), Bos(2020), and ISO (2025), the main reference. The aim of this paper is threefold. First, it focuses on implementing Korean AMR with the rich specification of QuantML (ISO, 2025) and its partially DRT-based semantics (Kamp and Reyle, 1993). Second, it supports the AMR multilingual development project by exploring methods for constructing a large-scale Korean AMR-annotated corpus. This line of research is necessary because Korean AMR resources remain severely underdeveloped. In addition, Korean’s agglutinative morphology and head-final syntax challenge AMR frameworks that are largely based on the analytic inflectional language English. Third, it advances the current state of the UMR 2026 multilingual shared task by contributing more fine-grained annotations of quantification specified by ISO QuantML for resource domain, individuation, distributivity, and determinacy, as well as by treating coreference and lexical or scope ambiguities in Korean.

The ISO working group on semantic annotation aims to adopt the UMR formalism to represent dynamic information involving motions and their embedding grounds. The paper details how ISO’s XML-based temporal and spatial annotations, involving motions and spatio-temporally conditioned event-paths, will be converted to AMR or UMR forms. It also attempts to enrich the representation of dynamic information with the integrated spatio-temporal annotation scheme that accommodates first-order dynamic logic, as briefly noted. The main motivation of such an effort is to make spatio-temporal annotations and related dynamic information easily understandable by artificial agents like robots to act. Our approach bridges ISO’s richly specified standards with the task-oriented expressiveness of UMR and dynamic logic. This integration paves the way for seamless downstream use of spatio-temporal annotations in dialogue systems, simulation environments, and embodied agents.