Some perspective on this subject:<p>- A graph database is essentially a relational database plus join recursion. Many modern relational databases support join recursion; support for graph models is ubiquitous, they just don&#x27;t call themselves &quot;graph databases&quot;. There are other reasons graph-like computation is relatively rare.<p>- There are some important types of relationships that are effectively not representable in graph data models. Relationships that have a topological nature, such as negative constraints, spatiotemporal, etc are notoriously problematic.<p>- General graph-like operations have terrible scalability and performance characteristics as commonly implemented. Consequently, people almost never organize their data models this way unless it is absolutely unavoidable. Even Facebook materializes common traversals as non-graphs so that they do not have to execute them dynamically. The heavy reliance on secondary indexing for performance in most graph databases ensures they will be marginal for large-scale data models.<p>- Semantic Web fails at scale because no two people map reality to definitions in the same way. This is particularly obvious at global scales because of cultural influences on how we interpret the world. If you&#x27;ve ever done global data model integrations, and I have, you quickly realize that the only values that are approximately consistent globally are physic-based measurements e.g. &quot;650nm wavelength&quot; (instead of &quot;red&quot;). The nodes have no common definition in practice, just commonly overlapping parts of countless subjective interpretations of the definition, which leads to pollution of the data model in real systems.