The illicit antiquities trade relies on various academic experts for its operations and legitimization. To counter this involvement, academics need to be made aware of how their services might support the trade. A suitable venue for future generations of professionals to obtain this knowledge is their university education. Hence, it is of interest to ask what is taught in university programs in relevant disciplines about the illicit antiquities trade and the forms of academic entanglement within it. This article focuses on course literature in university programs for conservators. It looks at both the official curriculum (explicit and intended teaching) and the hidden curriculum (implicit and unintended teaching) concerning illicit antiquities trade and conservators’ responsibilities vis-à-vis this trade. It finds that the coverage is limited and partly outdated. Further, textbooks might signal that it is acceptable to undertake the treatment of unprovenanced archaeological objects. The study suggests that it is relevant to ask not only if, but also how, the topic of professional responsibility is taught, and it argues that higher education, apart from teaching how to keep clear of activities that promote the trade, should provide the knowledge and skills to actively oppose it.

The transnational networks of the illicit and illegal antiquities trade are hard to perceive. We suggest representing the trade as a knowledge graph with multiple kinds of relationships that can be transformed by a neural architecture into a “knowledge graph embedding model.” The result is that the vectorization of the knowledge represented in the graph can be queried for missing “knowledge” of the trade by virtue of the various entities’ proximity in the multidimensional embedding space. In this article, we build a knowledge graph about the antiquities trade using a semantic annotation tool, drawing on the series of articles in the Trafficking Culture Project's online encyclopedia. We then use the AmpliGraph package, a series of tools for supervised machine learning (Costabello et al. 2019) to turn the graph into a knowledge graph embedding model. We query the model to predict new hypotheses and to cluster actors in the trade. The model suggests connections between actors and institutions hitherto unsuspected and not otherwise present in the original knowledge graph. This approach could hold enormous potential for illuminating the hidden corners of the illicit antiquities trade. The same method could be applied to other kinds of archaeological knowledge.