You Built a Knowledge Graph from 1M Documents. Can You Trust It? (memgraph.com)

A recent discussion sparked by Gurbinder Gill, co-founder of Corvic AI, highlighted the complexities and costs associated with building knowledge graphs from massive document collections, emphasizing the challenge of ensuring the accuracy of the extracted data. While creating a structural graph by leveraging inherent document organization (like pages and chunks) can be done efficiently, the transition to a knowledge graph—the aim to represent and connect the semantic meanings behind documents—introduces intricate tasks such as entity and relationship extraction, deduplication, and schema alignment that significantly inflate costs and complexity. The conversation, furthered by Toni Lastre from Memgraph, underlines that merely counting relationships and entities does not guarantee quality; validating the correctness of those relationships becomes increasingly challenging as document counts scale. With knowledge graphs underpinning critical applications—from analytics to retrieval systems—flawed connections can lead to pervasive misinformation. Strategies such as maintaining links back to original sources and enhancing validation processes could mitigate risks, but the fundamental concern remains: in large-scale applications, verifying the integrity of relationships in a knowledge graph is a daunting operational problem that calls for novel engineering approaches.