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  • By Franz Inc.
  • 8 November, 2021

AllegroGraph Name “Best Knowledge Graph” by KMWorld Readers’ Choice

AllegroGraph also wins “Finalist” position for “Best Cognitive Computing and AI Platform”

Lafayette, Calif., November 9, 2021 — Franz Inc., an early innovator in Artificial Intelligence (AI) and leading supplier of Graph Database technology for Entity-Event Knowledge Graph Solutions, today announced it has been named the  “Best Knowledge Graph” in the 2021 KMWorld Readers’ Choice Award voting. Additionally, AllegroGraph was considered a “Finalist” in the category of “Best Cognitive Computing and AI” platforms for the Readers’ Choice awards.

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

“As the stakes get higher for information-driven successes, businesses must make technology decisions from an increasingly diverse array of knowledge management offerings,” said Tom Hogan, Group Publisher at KMWorld. “The Readers’ Choice Awards put the spotlight on innovative and dependable solutions and services that can help companies solve pressing challenges and take advantage of new opportunities.”

“Franz Inc. is continually innovating and we are honored to receive this acknowledgement for our efforts in setting the pace for Knowledge Graph Solutions,” said Dr. Jans Aasman, CEO, Franz Inc. “We are seeing demand for Intelligent Data Fabrics take off across industries along with recognition from top technology analyst firms that Knowledge Graphs provide the critical foundation for Data Fabric solutions.  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.”

“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.”

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.

Gartner defines Data Fabric as a design concept that serves as an integrated layer (fabric) of data and connecting processes. “The emerging design concept called “data fabric” can be a robust solution to ever-present data management challenges, such as the high-cost and low-value data integration cycles, frequent maintenance of earlier integrations, the rising demand for real-time and event-driven data sharing and more,” said Mark Beyer, Distinguished VP Analyst at Gartner. (Source: Gartner, Data Fabric Architecture is Key to Modernizing Data Management and Integration, May 11, 2021)

Graph Analytics with Apache Spark

AllegroGraph 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.

AllegroGraph in Use

“We tried to modernize our product tracking system with 3rd party software solutions and in-house relational database applications, but without success because relational databases lack the ability to model complex relationships,” said Mel Yuson, Director Enterprise Architecture, Essilor AMERA, a multinational ophthalmic optics company and the world leader in the design, manufacture and distribution of lenses to correct or protect eyesight. “We needed the freedom of a schemaless graph database, like Franz’s AllegroGraph, which uniquely provides us the flexibility to evolve our data model and seamlessly add new applications to address rapid growth and changing needs at Essilor.”

“We developed and deployed to production our first AllegroGraph based application in only a few months after engaging Franz,” added Yuson. “We found AllegroGraph’s W3C standard SPARQL query language is much easier to use than SQL but most importantly, AllegroGraph is a very stable and highly scalable platform with its Multi-Master Replication cluster feature. Today, we deploy several AllegroGraph servers in the cloud, which easily handle 100,000 concurrent queries per minute at peak hours.”

“AllegroGraph’s support of Entity-Event Data Modeling is the most welcome innovation and addition to our arsenal in reimagining healthcare and implementing Precision Medicine,” said Dr. Parsa Mirhaji, Director of Center for Health Data Innovations at the Albert Einstein College of Medicine and Montefiore Health System, NY. “Precision Medicine is about moving away from statistical averages and broad-based patterns. It is about connecting many dots, from different contexts and throughout time, to support precision diagnosis and to recommend the precision care that can take into account all the subtle differences and nuisances of individuals and their personal experiences throughout their life. This technology is about saving lives, by leveraging data, context and analytics and is what Franz’s Entity-Event Data Modeling brings to the table.”

KM World Connect

Dr. Jans Aasman, CEO, Franz Inc., will be presenting a talk at KM World Connect titled, Graph Neural Networks for Text Classification and Relation Extraction on November 17, 2021 at 9:00am ET. You can Register for the Conference here.

About Franz Inc.

Franz Inc. is an early innovator in Artificial Intelligence (AI) and leading supplier of Graph Database technology with expert knowledge in developing and deploying Knowledge Graph solutions. The foundation for Knowledge Graphs and AI lies in the facets of semantic technology provided by AllegroGraph and Allegro CL. AllegroGraph is a graph based platform that enables businesses to extract sophisticated decision insights and predictive analytics from highly complex, distributed data that cannot be uncovered with conventional databases. Unlike traditional relational databases or other NoSQL databases, AllegroGraph employs semantic graph technologies that process data with contextual and conceptual intelligence. AllegroGraph is able to run queries of unprecedented complexity to support predictive analytics that help organizations make more informed, real-time decisions. AllegroGraph is utilized by dozens of the top Fortune 500 companies worldwide.