Collaborative-AI Knowledge Graph Generation: Taxonomization of IATE, the EU Terminology

Alena Vasilevich, Michael Wetzel, Stelios Mathioulakis, Jochen Hummel

Keywords: Collaborative taxonomization, Knowledge Graph UI, KG Construction Methods, Machine Learning

Abstract: Formalized knowledge is a powerful resource for AI projects, but it is usually created at great expense. Taxonomization is linking a flat set of concepts into a hierarchical knowledge graph, and in this work, we present our approach to semi-automatic generation of such concept maps, elevating a sub-domain of IATE terminology into a multilingual knowledge graph. We taxonomized a flat list of concepts within the COVID sub-domain, benchmarking two approaches to tackle this task: automatic concept map creation using an enhanced ML-powered language model and manual creation of the graph by a linguist expert. We dwell on advantages of the collaborative method, made easy by a user-friendly UI, and show how the achieved productivity rate can make taxonomization of large terminology databases economically viable.