Christoph Brandl¹, Jens Albrecht¹ and Renato Budinich², ¹Nuremberg Institute of Technology Georg Simon Ohm, Germany, ²Fraunhofer Supply Chain Services, Germany

The task of relation extraction aims at classifying the semantic relations between entities in a text. When coupled with named-entity recognition these can be used as the building blocks for an information extraction procedure that results in the construction of a Knowledge Graph. While many NLP libraries support named-entity recognition, there is no off-the-shelf solution for relation extraction.

In this paper, we evaluate and compare several state-of-the-art approaches on a subset of the FewRel data set as well as a manually annotated corpus. The custom corpus contains six relations from the area of market research and is available for public use. Our approach provides guidance for the selection of models and training data for relation extraction in realworld projects.

Relation Extraction, Knowledge Graph, Market Research.