The visual elegance of a knowledge graph is often little more than a facade that crumbles upon its first encounter with complex data topology. A recent experiment by computational linguist Sergey Slepukhin—applying the LightRAG framework to the Civil Code of the Russian Federation and 110 Supreme Court rulings—confirms that "out-of-the-box" systems deliver structural deadlocks rather than intelligent search. The average node degree barely exceeded one, effectively flattening an ambitious neural network into a primitive tree list.
The data tells a bleak story: 64.7% of nodes were either isolated or had only a single connection. Under these conditions, the graph fails to stitch disparate legal norms into the logical chains that GraphRAG proponents promise.
Two-thirds of the structure consists of "information islands" that lack global context. The situation is exacerbated by flaws in automated entity extraction, which turn the graph into a noisy data dump. The system stumbled on basics, creating bilingual duplicates where "Supreme Court of the Russian Federation" and its Russian equivalent coexisted as distinct entities. Meanwhile, the inherent hierarchy of the Civil Code articles was completely severed.
Key Takeaways for Business
Without manual normalization of duplicates and a rigid redefinition of legal taxonomy, this architecture offers no advantages over classic vector RAG. An "as-is" implementation in specialized domains results in a core graph that covers only 64.6% of entities, leaving a significant portion of the data inaccessible. Maintenance costs for such systems are significantly higher than traditional search methods without a guaranteed ROI.
Attempting to deploy these frameworks without domain-specific tuning makes search a lottery. For business leaders, this is a clear signal: implementing GraphRAG in niche industries requires deep investment in the entity extraction phase rather than just buying trendy licenses. If you fail to configure relationships at the intake stage, you won't get a "digital lawyer"—you'll get an expensive generator of random associations built on fragmented data.