Exploring In-Context Learning Capabilities of Foundation Models for
  Generating Knowledge Graphs from Text
Hanieh Khorashadizadeh Nandana Mihindukulasooriya Sanju Tiwari Jinghua Groppe Sven Groppe
Knowledge graphs can represent information about the real-world using
entities and their relations in a structured and semantically rich manner and
they enable a variety of downstream applications such as question-answering,
recommendation systems, semantic search, and advanced analytics. However, at
the moment, building a knowledge graph involves a lot of manual effort and thus
hinders their application in some situations and the automation of this process
might benefit especially for small organizations. Automatically generating
structured knowledge graphs from a large volume of natural language is still a
challenging task and the research on sub-tasks such as named entity extraction,
relation extraction, entity and relation linking, and knowledge graph
construction aims to improve the state of the art of automatic construction and
completion of knowledge graphs from text. The recent advancement of foundation
models with billions of parameters trained in a self-supervised manner with
large volumes of training data that can be adapted to a variety of downstream
tasks has helped to demonstrate high performance on a large range of Natural
Language Processing (NLP) tasks. In this context, one emerging paradigm is
in-context learning where a language model is used as it is with a prompt that
provides instructions and some examples to perform a task without changing the
parameters of the model using traditional approaches such as fine-tuning. This
way, no computing resources are needed for re-training/fine-tuning the models
and the engineering effort is minimal. Thus, it would be beneficial to utilize
such capabilities for generating knowledge graphs from text.