Searching for Physical Documents in Archival Repositories

Early retrieval systems were used to search physical media (e.g., paper) using manually created metadata. Modern ranked retrieval techniques are far more capable, but they require that content be either born digital or digitized. For physical content, searching metadata remains the state of the art. This paper seeks to change that, using a textual-edge graph neural network to learn relations between items from available metadata and from any content that has been digitized. Results show that substantial improvement over the best prior method can be achieved.