Every year, approximately around 2.5 million new scientific papers are published.With the rapidly growing publication trends, it is increasingly difficult to manuallysort through and keep track of the relevant research – a problem that is only moreacute in a multidisciplinary setting. The Open Research Knowledge Graph (ORKG)is a next-generation scholarly communication platform that aims to address thisissue by making knowledge about scholarly contributions machine-actionable, thusenabling completely new ways of human-machine assistance in comprehending re-search progress.As such, the ORKG is powered by a diverse spectrum of NLP services to assist theexpert users in structuring scholarly contributions and searching for the most rele-vant contributions. For a prospective recommendation service, this thesis examinesthe task of automated ORKG completion as an object extraction task from a givenpaper Abstract for a query ORKG predicate. As a main contribution of this thesis,automated ORKG completion is formulated as an extractive Question Answering(QA) machine learning objective under an open world assumption. Specifically, thetask attempted in this work is fixed-prompt Language Model (LM) tuning (LMT)for few-shot ORKG object prediction formulated as the well-known SQuAD extrac-tive QA objective. Three variants of BERT-based transfomer LMs are evaluated.To support the novel LMT task, this thesis introduces a scholarly QA dataset akinin characteristics to the SQuAD QA dataset generated semi-automatically from theORKG knowledge base. As a result, the BERT model variants when tested in vanilla setting versus after LMT, show a positive, significant performance uplift for auto-mated ORKG completion as an object completion task. This thesis offers a strong empirical basis for future research aiming at a production-ready automated ORKG completion model.