Three Bottlenecks of Traditional KG Construction

Traditional knowledge graph construction faces three persistent challenges that rule-based and statistical approaches struggle to overcome:

1. Scalability and data sparsity. Rule-based and supervised systems fail to generalize across domains. They work well within the bounds they were designed for and poorly everywhere else.

2. Expert dependency and rigidity. Schema and ontology design require substantial human intervention. The structures are precise but expensive to create and resistant to change.

3. Pipeline fragmentation. The stages, ontology engineering, extraction, fusion, are handled separately, causing cumulative error propagation. Mistakes early in the pipeline compound downstream.

These aren’t implementation problems to be engineered away. They’re structural tensions between precision and adaptability, between expert knowledge and scalable automation.

Related: 06-atom—llm-kg-paradigm-inversion, 06-atom—schema-based-vs-schema-free-extraction