Franz Inc. Delivers AI Knowledge Fabric Solutions for the Enterprise

Franz Inc., an early innovator in Artificial Intelligence (AI) and leading supplier of Graph Database technology for Entity-Event Knowledge Graph Solutions, announced it has been named a “2023 Trend Setting Product” by Database Trends and Applications. Additionally, AllegroGraph was recently named “Best Knowledge Graph” by KMWorld Readers’ Choice Award aware voting.

AllegroGraph provides organizations with essential Knowledge Graph solutions, including Graph Neural Networks, Graph Virtualization, GraphQL, Apache Spark graph analytics, and Kafka streaming graph pipelines. These capabilities exemplify AllegroGraph’s leadership in empowering data analytics professionals to derive business value out of Knowledge Graphs.

“Today’s data environments are highly diverse—residing on many platforms and requiring a variety of approaches to ensure data resiliency and availability,” said Tom Hogan, Group Publisher, Database Trends and Applications. “To help make the process of identifying useful products and services easier, each year, DBTA presents a list of ‘Trend-Setting Products.’ These products, platforms, and services range from long-established offerings that are evolving to meet the needs of their loyal constituents, to breakthrough technologies that may only be in the early stages of adoption.”

“Franz Inc. is continually innovating and we are honored to receive this acknowledgement for our efforts to deliver leading edge solutions in the Knowledge Graph Community,” said Dr. Jans Aasman, CEO, Franz Inc. “Organizations across a range of industries are realizing the critical role that Knowledge Graphs play in creating rich, yet flexible Enterprise Data Fabrics and AI-driven applications. AllegroGraph with FedShard uniquely provides companies with the foundational environment for delivering Graph based AI solutions with the ability to continually enrich and contextualize the understanding of data.”

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“AllegroGraph uniquely provides companies with the foundational environment for delivering AI Knowledge Graph solutions that continually enrich and contextualize the understanding of corporate data.”

— Jans Aasman, CEO, Franz Inc.

Knowledge Graphs for your Data Lakehouse

The emerging Data Lakehouse approach is bringing the best of Data Warehouses and Data Lakes in one simple platform to co-locate data from across the enterprise for cost effective analytics and AI use cases. But, despite the promise of Data Lakehouses, they still leave much of the data unconnected and in native form which can require significant effort to unlock its full potential.

Industry analysts recognize the power of a Semantic Layer in delivering integrated, trusted, and real-time views of enterprise data. Knowledge Graphs excel at delivering a Semantic Layer which unifies business data with knowledge bases, industry terms, and domain knowledge.

By overlaying a Knowledge Graph onto a Lakehouse architecture, the combination facilitates more flexible data operations, lowers data integration costs, and delivers powerful insights only possible when data is connected. Adding a Knowledge Graph to your Lakehouse will enable your organization to explore and exploit unknown connections across your data for richer analytics and enhanced Artificial Intelligence capabilities.

Franz’s AllegroGraph platform further extends this Knowledge Graph and Lakehouse combination with a novel Entity-Event Model. This production proven architecture puts core “entities” such as customers, patients, students, or people of interest at the center and then collects several layers of knowledge related to the entity as “events” in temporal context. Adding Franz’s Entity-Event Knowledge Graph to your Lakehouse delivers enhanced discovery, greatly reduced data complexity, and faster results – at scale.

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Graph Neural Networks

With AllegroGraph, 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.

Visualizing Knowledge Graphs

Gruff, which is available as a browser-based application or pre-integrated into AllegroGraph, is a no-code visual query application that enables users to create visual Knowledge Graphs that display data relationships in views driven by the user. Gruff’s visual query builder empowers both novice and expert users to create simple to highly complex queries without writing any code. The unique ‘Time Machine’ function within Gruff gives users the capability to explore temporal context and connections within data.

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