Ontology matching (OM) methods have a wide spectrum of applications, but most matchers find equivalence only correspondences. This limits their applicability in many real-world scenarios, where relations such as subclass, superclass, and overlap are needed. Such correspondences challenge not just the matchers. Alignments with correspondences beyond equivalence can have many equivalent forms and they can be imprecise or partially wrong, which challenges fair comparison. Furthermore, availability of real-world benchmark datasets is rare. Alignments between product classifications typically comprise correspondences beyond equivalence and reference mappings are available for standards like ETIM, eClass, GPC and UNSPSC. We introduce the notion of Product Master Data Model that captures classification systems and describe a construction method for benchmark datasets. The method is based on a class-based alignment representation, named isAmong alignment, which supports inference of correspondences (relation typing) and a standardized representation of alignments. It also supports a fair evaluation method, named isAmong evaluation, that assesses an alignment based on the degree of overlap with a reference alignment, measured using leaf-level coverage rather than weights. We present results that highlight current capabilities and gaps. The real-world benchmark datasets and the isAmong evaluation method are parts of a new OAEI track: Beyond Equivalence.