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2023

LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT

Meyer, Lars-Peter, Stadler, Claus, Frey, Johannes et al.

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Knowledge Graphs (KG) provide us with a structured, flexible, transparent, cross-system, and collaborative way of organizing our knowledge and data across various domains in society and industrial as well as scientific disciplines.

KGs surpass any other form of representation in terms of effectiveness.
However, Knowledge Graph Engineering (KGE) requires in-depth experiences of graph structures, web technologies, existing models and vocabularies, rule sets, logic, as well as best practices.
It also demands a significant amount of work.

LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT · Around

Built on

Haque, M.U., Dharmadasa, I., Sworna, Z.T., Rajapakse, R.N., Ahmad, H.: ”i think this is the most disruptive technology”: Exploring sentiments of chatgpt early adopters using twitter data (Dec 2022). https://doi.org/10.48550/ARXIV.2212.05856
2022
Earlier work this paper cites.
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y.T., Li, Y., Lundberg, S., Nori, H., Palangi, H., Ribeiro, M.T., Zhang, Y.: Sparks of artificial general intelligence: Early experiments with gpt-4 (Mar 2023). https://doi.org/10.48550/ARXIV.2303.12712
2023
Earlier work this paper cites.
Cagle, K.: Nine chatgpt tricks for knowledge graph workers. https://thecaglereport.com/2023/03/16/nine-chatgpt-tricks-for-knowledge-graph-workers/
2023
Earlier work this paper cites.

Similar

Ekaputra, F.J., Llugiqi, M., Sabou, M., Ekelhart, A., Paulheim, H., Breit, A., Revenko, A., Waltersdorfer, L., Farfar, K.E., Auer, S.: Describing and organizing semantic web and machine learning systems in the swemls-kg (Mar 2023). https://doi.org/10.48550/ARXIV.2303.15113
2023
Cited alongside, same era.
Kaliciński, K.: Create neo4j database model with chatgpt. https://neo4j.com/developer-blog/create-neo4j-database-model-with-chatgpt/
2023
Cited alongside, same era.

Then

Lin, W., Babyn, P., yan, Y., Zhang, W.: Context-based ontology modelling for database: Enabling chatgpt for semantic database management (Mar 2023). https://doi.org/10.48550/ARXIV.2303.07351
2023
Closest in time.
Omar, R., Mangukiya, O., Kalnis, P., Mansour, E.: Chatgpt versus traditional question answering for knowledge graphs: Current status and future directions towards knowledge graph chatbots (Feb 2023). https://doi.org/10.48550/ARXIV.2302.06466
2023
Closest in time.

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