Tiers of Abstraction and Audience in Cultural Heritage Data Modeling

When modeling data, and especially uncertain, historical and culturally sensitive data such as managed by museums, archives and special collections, there are always design and scoping decisions that can seriously impact the usability, precision and sustainability of any system built with that data. Too simple, and the data will not capture sufficient knowledge for it to be any more useful than a web page, and too complete and the data will be incomprehensible to anyone other than the data model architect. I have previously and widely argued for usability as a key indicator for success, and in this presentation will expand upon that to investigate two parallel sets of interactions: the different abstraction layers that must be considered when modeling knowledge, and the different audiences that must be taken into account when publishing that knowledge.

There are four tiers of abstraction in data modeling, and different systems have chosen to make some or all of these explicit. The presentation will cover several initiative, and their choices about the need for separation between conceptual model, ontology, vocabulary and application profile. Different choices in abstraction give different outcomes for the resulting data, which then has a direct impact on the ability to serve the needs of the four tiers of audience: Humans, Machines, the Network, and Research. These audiences have different requirements and expectations, especially when it comes to features such as modeling uncertainty and the provenance of the data, combined with social factors such as trust, credit and usage metrics.