Graphs without semantics are not enough

  • Why property graphs fall short

  • Can your graph software handle…?

  • 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.

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  • • 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
    • SOLR – native faceted search

  • 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.

Comparison of Property Graphs and Semantic Graphs


  •  AllegroGraph turns complex data into actionable business insights