In a new study, Amazon researchers propose a technique that improves the performance of knowledge graphs, which capture complex relationships.

Data-hungry AI applications are fed complex information, and that's where graph databases and knowledge graphs play a crucial role.

By combining ontology and large language model-driven techniques, engineers can build a knowledge graph that is easily queried and updatable.

Want to prepare for the future of search? Learn practical natural language processing (NLP) while building a simple knowledge graph from scratch.

Think of knowledge-graph-powered data catalogs as the search engine for the data in the enterprise.