Knowledge graphs are the next generation tool for helping businesses make critical decisions, based on harmonized knowledge models and data derived from siloed source systems. Due to the huge value generated by their data standardization and semantic modeling capabilities, knowledge graphs are most often associated with data integration, linking, unification and information reuse. As more and more organizations are turning to knowledge graphs for better data and content analytics, search and graph exploration become key requirements also.
For many years, two main advantages of labeled property graphs (LPG) have been pointed out: they can deal with properties on edges in the graph and they are good for graph traversal. They are gone now, given that the leading triplestores support:
- RDF-star, which offers a simple and efficient mechanism to attach metadata to the edges of a graph, e.g. weights, access restrictions and provenance information.
- SPARQL extensions that allow for exploration of multi-hop relationships in graphs.
Long story short, RDF engines check all the boxes: simple-yet-powerful graph model, standard schema and query languages, formal semantics, efficient graph traversal, analytics and reasoning, packed with all the enterprise features. There are a couple of cases where LPGs still have an edge: micromanaged exploration using Gremlin and heavy analytics for wardrobes with TBs of RAM.
Join Atanas Kiryakov in his talk at the Connected Data World 2021 virtual conference to get convinced.