Large amounts of RDF/S data are produced and published lately, and several modern applications require the provision of versioning and archiving services over such datasets. In this paper we propose a novel storage index for archiving versions of such datasets, called CPOI (compact partial order index), that exploits the fact that an RDF Knowledge Base (KB), is a graph (or equivalently a set of triples), and thus it has not a unique serialization (as it happens with text). If we want to keep stored several versions we actually want to store multiple sets of triples. CPOI is a data structure for storing such sets aiming at reducing the storage space since this is important not only for reducing storage costs, but also for reducing the various communication costs and enabling hosting in main memory (and thus processing efficiently) large quantities of data. CPOI is based on a partial order structure over sets of triple identifiers, where the triple identifiers are represented in a gapped form using variable length encoding schemes. For this index we evaluate analytically and experimentally various identifier assignment techniques and their space savings. The results show significant storage savings, specifically, the storage space of the compressed sets in large and realistic synthetic datasets is about the 8% of the size of the uncompressed sets.