Why property graphs fall short

For simple graph oriented data relationships, a non-semantic (or property graph) database approach might solve a single dimensional problem like: shortest path, one-to-many relationships, weighted elements, structured inter-relationships.

But rarely are problems and queries that simple. Real world data is highly complex, multi-dimensional and needs the powerful additional features of a semantic graph solution.

Also, property graphs require the fixed definition of classes/objects, types and nodes – basically a fixed schema. If the data or data sources ever change, those changes need to be coded before any new data can be accessed.

Can your graph software handle…?

    • SPARQL 1.1 query language support
    • Linked Open Data support – with RDF
    • Unstructured, free field text and document processing
    • Geo-spatial queries
    • Temporal queries
    • Social Network Analysis
    • Reasoning/Inferencing
    • Machine learning
    • Bayesian belief networks/probabilistic processing
    • Prolog with complex rule and logic processing
    • Federated, multi-database integration
    • Predictive analytics
    • Hadoop/Cassandra/MongoDB support for massive data/document stores
    • Dis-ambiguation of terms, names, locations
    • JSON/JSON-LD
    • SOLR – native faceted search

Comparison of Property Graphs and Semantic Graphs

AllegroGraph turns complex data into actionable business insights