The accurate representation of association relationships remains a fundamental concern in conceptual modeling. While most modeling languages provide constructs for associations, they differ substantially in terms of semantic richness, structural granularity, and ontological commitments. This paper investigates the semantic complexity of named (associative) relationships by analyzing selected features of association modeling across a range of conceptual modeling languages and metamodels, including UML, EER, RDF Schema, ORM, and AOM. To facilitate a uniform and semantically grounded analysis, we introduce the Conceptual Layer of Metamodels (CLoM) (Conceptual Layer of Metamodels), an ontology-driven abstraction for characterizing the semantic primitives underlying association constructs. Each modeling feature is evaluated in terms of its ontological status within CLoM, allowing us to isolate conceptual capabilities and limitations of individual metamodels. The core contribution of this work is a comparative matrix capturing the presence and interpretation of key semantic features—such as arity, identity, role inheritance, multiplicity, and role constraints—across selected modeling approaches. The paper concludes with a discussion on the semantic expressiveness of the examined metamodels and highlights potential directions for improving the ontological robustness of association modeling.