1. The Entity Resolution Bottleneck in Knowledge Graphs
When constructing enterprise Knowledge Graphs from disparate corporate data sources (CRMs, ERPs, support tickets, internal wikis), the same real-world entity appears under dozens of subtle variations: "International Business Machines", "IBM Corp", "IBM India Ltd", and "Big Blue". If a knowledge graph treats these as separate nodes, relational queries fail to aggregate insights, fracturing institutional intelligence.
2. Vector-Augmented Entity Resolution Pipeline
Entity Resolution (deduplication) combines graph topology with dense vector embeddings: 1. Extract potential entity candidates; 2. Embed entity names, aliases, and contextual descriptions into high-dimensional vector space; 3. Perform fast k-NN nearest-neighbor search to identify high-similarity candidate pairs (cosine similarity > 0.88).
3. LLM-Assisted Graph Link Prediction and Disambiguation
Candidate pairs with high vector similarity are passed to a lightweight reasoning model along with their immediate 1-hop neighbor relationships in the graph. The model evaluates whether the two nodes represent the identical corporate entity or two distinct subsidiaries. Upon confirmation, the graph engine executes an atomic merge, consolidating duplicate edges and redirecting relationships to a canonical master node.
4. Continuous Real-Time Graph Curation
By scheduling automated nightly entity resolution jobs across enterprise Neo4j/Memgraph clusters, organizations maintain clean, unified knowledge networks that power high-precision GraphRAG, fraud detection, and automated enterprise intelligence search.
