The rich internal structure of LPGs results in more efficient storage and faster data traversals and queries.

however, due to the arbitrary data structure design, LPGs are not as practical for modelling ontologies and other structured data representations as RDF models.

LPG edges/connections can have types and attributes (properties as the name suggests) natively, making the LPG data structure more dense, compact, and informative compared to RDF.

There are two camps of Graph database, one side is RDF, where they are strict with their format, and somewhat limited for their extensibility. The other side is LPG, where they can define labels to the relationships.

SQL/PGQ reduces the difference in functionality between relational DBMSs and native graph DBMSs. Basically, this new feature makes it easier to query data in tables as if it were in a graph database, providing a possibly more intuitive alternative to writing complex join queries.

There is an ongoing debate about which graph data model is best, and in this blog post, we’ll explore why RDF (Resource Description Framework) stands out as the superior choice for building more sustainable and scalable knowledge graphs over LPG (Labeled Property Graphs).