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Knowledge graphs can represent information about the real-world usingentities and their relations in a structured and semantically rich manner andthey enable a variety of downstream applications such as question-answering,recommendation systems, semantic search, and advanced analytics. However, atthe moment, building a knowledge graph involves a lot of manual effort and thushinders their application in some situations and the automation of this processmight benefit especially for small organizations. Automatically generatingstructured knowledge graphs from a large volume of natural language is still achallenging task and the research on sub-tasks such as named entity extraction,relation extraction, entity and relation linking, and knowledge graphconstruction aims to improve the state of the art of automatic construction andcompletion of knowledge graphs from text. The recent advancement of foundationmodels with billions of parameters trained in a self-supervised manner withlarge volumes of training data that can be adapted to a variety of downstreamtasks has helped to demonstrate high performance on a large range of NaturalLanguage Processing (NLP) tasks. In this context, one emerging paradigm isin-context learning where a language model is used as it is with a prompt thatprovides instructions and some examples to perform a task without changing theparameters of the model using traditional approaches such as fine-tuning. Thisway, no computing resources are needed for re-training/fine-tuning the modelsand the engineering effort is minimal. Thus, it would be beneficial to utilizesuch capabilities for generating knowledge graphs from text.

Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text