KG-BERT: BERT for Knowledge Graph Completion

Source: Dev.to

Overview

KG‑BERT reads knowledge‑graph triples (subject, relation, object) as short sentences and assigns a simple score indicating how likely the fact is true. It leverages a pre‑trained language model that already knows a large vocabulary, feeding it the textual descriptions of entities and relations instead of relying on messy tabular data.

By doing so, KG‑BERT can:

• Detect incorrect or missing links in a knowledge graph (link prediction) without extensive rule‑writing.
• Achieve strong performance across various benchmark tests, often surpassing older methods.
• Be applied to tasks such as suggesting where entities belong, identifying appropriate relations, and cleaning data for search engines, virtual assistants, and other applications.

Think of KG‑BERT as a reader that quickly fills in blank facts, making knowledge graphs more complete and easier to use.

Further reading

KG‑BERT: BERT for Knowledge Graph Completion