Graph Path Search with GraphDB 9.9 and metaphactory 4.3

Hands-on tutorial on how GraphDB enters the Property Graph’s stronghold: exploring paths between nodes. And a demonstration of how metaphactory brings it to the end-users in an interactive and intuitive manner.

The recording of this webinar is available on YouTube.

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 the first part of this webinar, we will explain why graph path search is a computationally expensive task and will present our graph path search implementation. We will compare how the different RDF and property graph databases implement it and will 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 will test GraphDB and other engines against LDBC’s Semantic Network Benchmark (see the snapshot above) – 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, our partners from metaphacts will demonstrate how the new graph path search algorithm available in GraphDB 9.9 can be exposed to end-users with the upcoming metaphactory 4.3 release. They will 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.

What You Will Learn:
  • Graph path search fundamentals
  • Features in the “vanilla” SPARQL 1.1 limiting the exploration of paths in a graph
  • New extension in GraphDB 9.9 to enable complex graph pattern search in SPARQL compliant manner
  • Testing GraphDB performance against LDBC’s Social Network Benchmark
  • Demonstration on how graph path search is exposed to end-users in the upcoming metaphactory 4.3 release
Who is this webinar for:
  • Any novice or advanced RDF database users
  • Technologists willing to compare Property Graph vs. RDF
  • Data scientists and everyone involved in graph analytics
  • Current and future users of GraphDB and metaphactory
Expected duration:
  • 45 minutes presentation
  • 15 minutes Q&A session

About The Speaker

Tomas Kovachev

Tomas Kovachev

Lead Software Developer

Tomas Kovachev is the Lead Software Developer of GraphDB. He has temporarily given up his financial and accounting profession for coding complex data graph structures with minimal memory consumption. There is definitely no better person than Tomas to present the graph path search features, where he implemented significant parts of the code logic.

Sebastian Schmidt

Sebastian Schmidt

CEO, metaphacts

Sebastian has 20 years of experience in the IT industry throughout which he has held leading positions in software engineering, pre-sales, consulting and product management. As Co-CEO at metaphacts, Sebastian leads customer- and partner-facing activities and is responsible for driving digital transformation initiatives with customers across Europe, North America and Asia.