#Entity Resolution

Entity resolution identifies and merges records that refer to the same real-world entity across data sources. Articles cover BigQuery-based entity resolution, knowledge graph construction, and the matching algorithms that deduplicate and link records in analytical workloads.

2 posts tagged with entity resolution. ← All posts

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Pratik Dhanave · ·7 min read

Building a Knowledge Graph — Construction and Entity Resolution

The hardest part of a knowledge graph isn't querying it or designing the ontology — it's building it from messy, real-world data. Most of your knowledge lives in databases that don't connect, documents that don't parse, and records that refer to the same thing by different names. Construction is the engineering of turning that mess into a clean, connected graph, and its central, unglamorous challenge is entity resolution: deciding when two records are actually the same thing. This post is about the work that makes or breaks a knowledge graph.

The hard part isn't the ontology or the queries — it's construction. The pipeline from heterogeneous sources, and its make-or-break challenge: entity resolution (deciding when two records are the same thing). Under-merging fragments; over-merging asserts false connections; connectivity amplifies every error.

#Knowledge Graphs#Entity Resolution#Data Engineering#Data Quality#Ontology#Semantic Web

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.