Traverse connected data to discover lineage and context

Sometimes the answer you need is expressible as a simple fact: What is the social security number of person X? What is the risk factor for investment Y? But in other cases, understanding and using the connectedness of the knowledge graph — using the path from one node to another — is key to finding the answer to your questions. For example, what does the supply chain look like for this batch of troublesome parts? Or, how are these two financial organizations related to these five transactions and these three shadowy political figures?

These questions are directly expressible in Stardog using Pathfinder, a concise syntax in SPARQL for path queries. Pathfinder can traverse the graph—across materialized and virtualized sources—and find different kinds of paths, including: between a fixed pair of nodes or between all nodes; any path or only the shortest path, including based on weight; and over inferred relationships, where there is not an explicit edge between two nodes, but there is a graph pattern corresponding to a relationship that links those nodes. Pathfinder shows the intermediate nodes in a path in addition to the start and end points and can be restricted to certain types of paths or number of hops.

  • Investigate graph technologies if you need to enrich multiple datasets with semantics and to automate or augment their unification. This allows richer context provisioning, including traversal across data silos, relationship discovery and qualification, all with the superior query performance that is a must for real-time and continuous intelligence applications.

    - Gartner “Predicts 2020: Data and Analytics Strategies — Invest, Influence, and Impact,” Lydia Clougherty Jones, et al, 6 December 2019

Pathfinder enables several different use cases:

Pathfinder in Stardog combines the most complete graph query language with the most complete data unification platform.


    Medical research institute QIMR Berghofer uses Pathfinder to find connections in its research data. The traditional data models QIMR was using stifled innovation. Now, QIMR can chart a path from sample, to sequencing library, to data from sequencing machine, and on to the next layer of analysis, quickly. This lets researchers connect all the pieces easily and get to life-saving answers faster.

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