Investing Knowledge Graph Series: Why Entity Resolution Breaks When Merging Multi-Source Data

A developer building an investing knowledge graph shares insights from two real-world conversations that shaped the final part of their series. One discussion involved a financial news aggregation system merging data from English and Chinese sources, such as matching 'Tesla, Inc.' with '特斯拉', highlighting challenges in cross-language entity blocking. The other involved a KYC sanctions screening system, where the stakes of a false negative — an entity wrongly cleared — are far more serious than in a typical knowledge graph. While the core entity resolution architecture transfers across both use cases, the author cautions that model thresholds and training data must be domain-specific, especially for compliance tools. Cross-language name matching and alias generation during ingestion are identified as key technical hurdles that standard string normalization alone cannot solve.