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
    • JSON/JSON-LD
    • 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

AG-vs-prop-graph


  •  AllegroGraph turns complex data into actionable business insights