Page History
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- Daniel Burkhardt (FSTI)
- Robert David (SWC)
- Diego Collarana (FIT)
- Daniel Baldassare (doctima)
- Michael Wetzel (Coreon)
Draft Robert David:
- Initial RAG idea: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- RAG is commonly used with vector databases.
- can only grasp semantic similarity represented in the document content
- only unstructured data
- vector distance instead of a DB search limits the retrieval capabilities
- Graph RAG uses knowledge graphs as part of the RAG system
- KGs for retrieval (directly), meaning the database is storing KG data
- KGs for retrieval via a semantic layer, potentially retrieving over different data sources of structured and unstructured data
- KGs for augmenting the retrieval, meaning the queries to some database is modified via KG data
- Via Graph RAG, we can
- ingest additional semantic background knowledge (knowledge model) not represented in the data itself
- additional related knowledge based on defined paths (rule-based inference)
- focus on certain aspects of a data set for the retrieval (search configuration)
- personalization: represent different roles for retrieval via ingesting role description data into the retrieval (especially important in an enterprise environment)
- reasoning
- linked data makes factual knowledge related to the LLM-generated knowledge and thereby provide a means to check for correctness
- explainable AI: provide justifications via KG
- consolidate different data sources: unstructured, semi-structured, structured (enterprise knowledge graph scenario)
- doing the actual retrieval via KG queries: SPARQL
- hybrid retrieval: combine KG-based retrieval with vector databases or search indexes
- ingest additional semantic background knowledge (knowledge model) not represented in the data itself
Hybrid Retrieval Combining KGs and Dense Vectors
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