Starting with a knowledge graph constructed from unstructured data with the help of LLMs, we'll address the common challenge of duplicated entities resulting from variations in extracted names and identifiers.
Discover how Neo4j’s Node Similarity algorithms can be leveraged to identify and merge these entities, enhancing the accuracy and usability of your data.
Guest: Johannes Jolkkonen @johannesjolkkonen
Graphstuff.FM: https://graphstuff.fm/episodes/rag-databases-with-johannes-jolkkonen-when-to-choose-a-graph-database-vs-alternative-vector-or-relational-stores
Node Similarity: https://development.neo4j.dev/docs/graph-data-science/current/algorithms/node-similarity/
fastRP: https://development.neo4j.dev/docs/graph-data-science/current/machine-learning/node-embeddings/fastrp/
Entity Resolution and Deduping: Best Practices From Neo4j's Field Team https://youtu.be/O9Qpz6dI9zg
#neo4j #graphdatabase #entity #duplicate #genai #llm #knowledgegraph