Abstract

The increasing capabilities of Large Language Models (LLMs) allows us to rethink how we architect systems for and abased on knowledge graphs. LLMs can be used as encoders for unstructured information such and images and text, which allows to take advantage of the attributes of entities in a Knowledge Graph. Furthermore, LLMs provide increasingly robust information extractors allowing information to be extracted on-the-fly. Likewise, they can be used as flexible components of common data wrangling tasks such as entity resolution. Finally, LLMs contain knowledge in their parameters providing a new source of knowledge. Together these suggest the ability to create new architectures that can take advantage the of different characteristics of information sources.

Metadata

publication

CEUR WORKSHOP PROCEEDINGS 3747, 1-4, 2024

year

2024

publication date

2024

authors

Sanju Tiwari, Nandana Mihindukulasooriya, Francesco Osborne, Dimitris Kontokostas, Jennifer D'Souza, Mayank Kejriwal

link

https://boa.unimib.it/handle/10281/521187

resource_link

https://boa.unimib.it/bitstream/10281/521187/1/Tiwari-2024-TEXT2KG-VoR.pdf

journal

CEUR WORKSHOP PROCEEDINGS

volume

3747

pages

1-4

publisher

CEUR-WS