Franz Introduces Graph Neural Networks in Latest AllegroGraph Release

Franz Inc., an early innovator in AI and leading supplier of graph database technology, is releasing AllegroGraph 7.2, providing organizations with essential data fabric tools, including graph neural networks, graph virtualization, Apache Spark graph analytics, and streaming graph pipelines.

“The ability to create Graph Neural Networks within the AllegroGraph platform opens up the next level of AI to data analytics professionals with the ability to produce the best prescriptive outcomes,” said Dr. Jans Aasman, CEO of Franz Inc. “GNNs are ideal for applying machine learning’s advanced pattern recognition to high-dimensional, non-Euclidian datasets that are too complex for other machine learning types. Organizations get two forms of reasoning in one framework by fusing GNN reasoning capabilities around relationship predictions, entity classifications, and graph clustering, with classic semantic inferencing available in AllegroGraph Knowledge Graphs. Automatically mixing and matching these two types of reasoning is next level AI and is the basis for predicting the best prescriptive outcome for any business event based on context at scale.”

With AllegroGraph 7.2, users can create graph neural networks (GNNs) and take advantage of a mature AI approach for knowledge graph enrichment via text processing for news classification, question and answer, search result organization, event prediction, and more.

GNNs created in AllegroGraph enhance neural network methods by processing the graph data through rounds of message passing, as such, the nodes know more about their own features as well as neighbor nodes. This creates an even more accurate representation of the entire graph network. AllegroGraph GNNs advance text classification and relationship extraction for enhancing enterprise-wide Data Fabrics.

AllegroGraph 7.2 allows users to easily virtualize data as part of their AllegroGraph Knowledge Graph solution. When graphs are virtual, the data remains in the source system and is easily linked and queried with other data stored directly in AllegroGraph.

Any data source with a supported JDBC driver can be integrated into an AllegroGraph Knowledge Graph, including databases (e.g., Oracle Database, MySQL, Apache Cassandra, AWS Athena, Microsoft SQL Server, MongoDB); BI tools (e.g., IBM Cognos, Microsoft PowerBI, RapidMiner, Tableau); CRM systems (e.g., Dynamics CRM, Netsuite, Salesforce, SugarCRM); cloud services (e.g., Active Directory, AWS Management, Facebook, Marketo, Microsoft Teams, SAP, ServiceNow) and shared data files (e.g., Box, Gmail, Google Drive, Office365).

AllegroGraph 7.2 can now be used seamlessly with Apache Kafka, an open-source distributed event streaming platform for high-performance data pipelines, streaming analytics, data integration and mission-critical applications. By coupling AllegroGraph with Apache Kafka, users can create a real-time decision engine that produces real-time event streams based on computations that trigger specific actions. AllegroGraph accepts incoming events, executes instant queries and analytics on the new data and then stores events and results.

Additionally, AllegroGraph 7.2 enables users to export data out of the Knowledge Graph and then perform graph analytics with Apache Spark, one of the most popular platforms for large-scale data processing. Users immediately gain machine learning and SQL database solutions as well as GraphX and GraphFrames, two frameworks for running graph compute operations on data.

A key benefit of using Apache Spark for graph analytics within AllegroGraph is that it is built on top of Hadoop MapReduce and extends the MapReduce model to efficiently use more types of computations. Users can access interfaces (including interactive shells) for programming entire clusters with implicit data parallelism and fault-tolerance.

For more information about this release, visit