Extraction of a Knowledge Graph from French Cultural Heritage Documents

Erwan Marchand, Michel Gagnon, Amal Zouaq

Abstract: Cultural heritage in Quebec is often represented as collections of French documents that contain a lot of valuable, yet unstructured, data. One of the current aims of the Quebec Ministry of Culture and Communications (MCCQ) is to learn a knowledge graph from unstructured documents to offer an integrated semantic portal on Quebec’s cultural heritage. In the context of this project, we describe a machine learning and open information extraction approach that leverages named entity extraction and open relation extraction in English to extract a knowledge graph from French documents. We also enhance the generic entities that can be recognized in texts with domain-related types.