Graph path search with GraphDB 9.9 and metaphactory 4.3

In this webinar, Ontotext and metaphacts present the graph path search implementation in GraphDB and demonstrate how the new graph path search algorithm available in GraphDB can be exposed to end users with metaphactory.

Webinar overview

Knowledge Graphs have become a popular trend in the representation of complex data, metadata and content. They offer comprehensive, consistent and unified views to information scattered across different divisions, systems and paradigms. Unsurprisingly, Knowledge Graphs are most often associated with data integration, linking, unification and information reuse because of the huge value generated by their data standardization and semantic modeling capabilities. Still, the semantic data integration task is only part of the story.

Search and graph exploration are key tools for successfully utilizing knowledge graphs. Path finding between resources can additionally enable more complex use cases which previously left users struggling. Ontotext and metaphacts can generate a lot of value on top of Knowledge Graphs in analytical use cases through graph path search and interactive visualization.

In this webinar, Ontotext and metaphacts explain why graph path search is a computationally expensive task and present the graph path search implementation in GraphDB. We compare how the different RDF and property graph databases implement it and dive into how GraphDB extends the SPARQL 1.1 standard to fully support all significant graph path search use cases.

In order to give you a feeling about the scalability and efficiency of our implementation, we test GraphDB and other engines against LDBC's Semantic Network Benchmark – one of the most advanced benchmarks for graph analytics, implemented for Property Graphs/Cypher, RDF/SPARQL and even SQL.

In the second part of the webinar, we demonstrate how the new graph path search algorithm available in GraphDB 9.9 can be exposed to end users with the  metaphactory 4.3 release. We share a specific use case, taken from the clinical trial domain: How a researcher can find connections between study investigators and various other resources, like targets and diseases, which will allow them to further investigate discovered studies or medications.

Watch the webinar recording here »


Tomas Kovachev - Software Developer, Ontotext

Sebastian Schmidt - CEO, metaphacts