Zuo, Zhe; Loster, Michael; Krestel, Ralf; Naumann, Felix
Proceedings of the Conference "Lernen, Wissen, Daten, Analysen" (LWDA)
This paper establishes a semi-supervised strategy for extracting various types of complex business relationships from textual data by using only a few manually provided company seed pairs that exemplify the target relationship. Additionally, we offer a solution for determining the direction of asymmetric relationships, such as “ownership of”. We improve the reliability of the extraction process by using a holistic pattern identification method that classifies the generated extraction patterns. Our experiments show that we can accurately and reliably extract new entity pairs occurring in the target relationship by using as few as five labeled seed pairs.