Relationship Prediction in a Knowledge Graph Embedding Model of the Illicit Antiquities Trade

Abstract

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.