Knowledge Graph Chunking

Instead of splitting by character count or headings, this strategy identifies and isolates chunks based on named entities (people, organizations, locations) and their immediate relational context. Each chunk becomes a subgraph centered on a primary entity, ensuring that the retrieved context is a complete factual statement rather than a fragmented sentence. This prevents the chunk contamination that occurs when a single vector chunk mixes unrelated topics.

Chunk boundaries are determined by the integrity of semantic triples (subject-predicate-object). The algorithm refuses to split a sentence or paragraph if doing so would sever a direct relationship between two entities. This preserves chunk coherence at a logical level, ensuring that a retrieved chunk always contains a complete atomic fact. The result is a chunk graph where nodes are text segments and edges represent explicit, typed relationships like 'employed_by' or 'headquartered_in'.

Because chunks are stored as interconnected nodes in a graph structure, retrieval is not limited to single-shot vector search. A query can traverse the chunk graph to perform multi-hop reasoning:

• Start with a chunk about 'Acme Corp'
• Traverse the 'acquired_by' edge to a chunk about 'Global Industries'
• Traverse the 'CEO_of' edge to a chunk about 'Jane Smith' This enables answering complex questions like 'Who is the CEO of the company that acquired Acme Corp?' without requiring the entire document to be loaded into the context window.

Knowledge Graph Chunking allows retrieval to be performed using Cypher or SPARQL queries alongside vector similarity. A hybrid retrieval pipeline can first execute a precise graph query to find all chunks linked to a specific entity ID, then re-rank those candidates using embedding similarity. This combines the precision of a deterministic database lookup with the semantic flexibility of dense retrieval, dramatically improving recall for queries that hinge on specific, known entities.

The chunking process dynamically maps extracted relationships to an existing enterprise ontology or taxonomy. As text is segmented, entities are disambiguated and linked to canonical nodes in the master knowledge graph. This ensures that a chunk about 'Apple' the company is never confused with 'Apple' the fruit, and that all chunks are indexed under the correct, authoritative entity identifier. This alignment is critical for chunk attribution and citation accuracy.

Each graph chunk is automatically enriched with structured metadata extracted from the source text:

• Temporal validity: Dates of facts (e.g., 'CEO from 2019-2023')
• Provenance: Source document, author, and confidence score
• Entity roles: The specific function an entity plays in the chunk's context This metadata enrichment allows for filtered retrieval, such as 'find all chunks about Acme Corp's financial performance in Q3 2023,' without relying on noisy keyword matching.