In the multi-level type modeling community, claims that most enterprise application systems use ontologically multi-level types are ubiquitous. To be able to empirically verify this claim one needs to be able to expose the (often underlying) ontological structure and show that it does, indeed, make a commitment to multi-level types. We have not been able to find any published data showing this being done. From a top-level ontology requirements perspective, checking this multi-level type claim is worthwhile. If the datasets for which the top-level ontology is required are ontologically committed to multi-level types, then this is a requirement for the top-level ontology. In this paper, we both present some empirical evidence that this ubiquitous claim is correct as well as describing the process we used to expose the underlying ontological commitments and examine them. We describe how we use the bCLEARer process to analyse the UNICLASS classifications making their implicit ontological commitments explicit. We show how this reveals the requirements for two general ontological commitments; higher-order types and first-class relations. This establishes a requirement for a top-level ontology that includes the UNICLASS classification to be able to accommodate these requirements. From a multi-level type perspective, we have established that the bCLEARer entification process can identify underlying ontological commitments to multi-level type that do not exist in the surface linguistic structure. So, we have a process that we can reuse on other datasets and application systems to help empirically verify the claim that ontological multi-level types are ubiquitous.

bCLEARer stands at the forefront of digital transformation, championing an evolutionary approach to harnessing digitization and digitalization opportunities. It guides information on a transformative journey, curating its evolution into fitter forms, ones more suited for computing, that deliver increased value.

To accomplish this, bCLEARer has evolved an architecture framework for semantic data pipelines, along with a methodology for engineering these pipelines.

A while ago, a client engaged us to crystallise our then current working documentation on the architecture framework into an eManual for their bCLEARer programme. What follows is a sanitized version of that manual, capturing the architecture as it existed then. Though bCLEARer continues to evolve, the core principles in this eManual remain relevant.