An early innovator in Artificial Intelligence (AI), Franz Inc. is a 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.

Franz Inc. provides a variety of services as part of its Knowledge Graph platform solution– from architectural consulting and technical seminars to training.

Franz’s flagship product, AllegroGraph, provides the necessary power and flexibility to address high-security data environments such as HIPAA access controls, privacy rules for banks, and security models for policing, intelligence, and government.

The Prolific Leader

A Ph.D psychologist and expert in the Cognitive Science Jans Aasman is the CEO of Franz.com. As both a scientist and CEO, Dr. Aasman continues to break ground in the areas of Artificial Intelligence and Semantic Databases as he works hand-in-hand with organizations such as Montefiore Medical Center, Blue Cross/Blue Shield, Wolters Kluwer, Merck, AstraZeneca, N3 Solutions, BAE Systems as well as US and Foreign governments.

Dr. Aasman is a frequent speaker within the Graph and AI technology industry and has authored multiple research papers and numerous patents.

Dr. Aasman spent a large part of his professional life in telecommunications research, specializing in applied Artificial Intelligence projects and intelligent user interfaces. He gathered patents in the areas of speech technology, multimodal user interaction, recommendation engines while developing precursor technology for today’s smart devices.

The Unique Product

Franz stands apart from other Graph database vendors by offering Semantic Graph database technology based on RDF, a W3C standard model for data interchange on the Web. The company’s flagship product, AllegroGraph, is a high-performance Semantic Graph Database that processes data with contextual and conceptual intelligence to perform queries of unprecedented complexity and support predictive analytics that help companies make better, real-time decisions. Unlike relational databases, AllegroGraph provides the unique ability to infer or understand the meaning of information and link new information automatically, without manual user intervention, coding or the database being explicitly pre-structured.

AllegroGraph—Graph Database for AI Knowledge Graphs

Artificial Intelligence (AI) is one of the top investment areas for companies looking to improve ROI on operations and products, and to create customer 360 views. Using AI to create “Enterprise Knowledge” and link it across the Enterprise to create a “Knowledge Graph” is a key differentiator for companies in an ever-increasing competitive landscape.

The foundation for Knowledge Graphs and Artificial Intelligence lies in the facets of semantic technology provided by Franz’s AllegroGraph database. Graph databases, such as AllegroGraph, provide the core technology environment to enrich and contextualize the understanding of data. The ability to easily integrate new knowledge is the crux of the Knowledge Graph and depends entirely on semantic technologies.

AllegroGraph enables organizations to gain sophisticated insights and predictive analysis from highly complex, distributed data – exceeding the possibilities of conventional databases and laying the foundation for Knowledge Graphs and Artificial Intelligence solutions.

Unlike traditional relational databases or other NoSQL databases, AllegroGraph employs 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.

Gartner recently identified Knowledge Graphs as a key new technology in both their Hype Cycle for Artificial Intelligence and Hype Cycle for Emerging Technologies. Gartner’s Hype Cycle for Artificial Intelligence, 2018 states, “The rising role of content and context for delivering insights with AI technologies, as well as recent knowledge graph offerings for AI applications have pulled knowledge graphs to the surface.”

Rapidly connecting new knowledge is a key competitive advantage delivered by Knowledge Graphs and the core technology depends on semantic graph databases.” said Jans Aasman, CEO of Franz.

A Knowledge Graph represents a knowledge domain and connects things of different types in a systematic way by encoding knowledge arranged in a network of nodes and links rather than tables of rows and columns. People and machines can benefit from Knowledge Graphs by dynamically growing semantic network of facts about things and can use it for data integration, knowledge discovery, and in-depth analyses.

Gartner also featured AllegroGraph in a recent report that explains the importance of using semantic technology to drive business value out of data. In the report, Gartner’s Analyst noted, “Unprecedented levels of data scale and distribution are making it almost impossible for organizations to effectively exploit their data assets. Data and analytics leaders must adopt a semantic approach to their enterprise data assets or face losing the battle for competitive advantage.” (Source: Gartner, How to Use Semantics to Drive the Business Value of Your Data, Guido De Simoni, November 27, 2018.)

In today’s data-driven environments, the ability to analyze data from diverse sources is becoming a top priority for every CIO. Organizations in a myriad of industries including healthcare, intelligence/defense, life sciences and financial services need to quickly analyze streams of structured and unstructured data from heterogeneous sources to effectively compete and make informed decisions. However, traditional relational database technology is inadequate for today’s complex analytics requirements.

Forrester Research stated, “Graph databases are a powerful optimized technology that link billions of pieces of connected data to help create new sources of value for customers and increase operational agility for customer service. Because graph databases track connections among entities and offer links to get more detailed information, they are well-suited for scenarios in which relationships are important, such as cybersecurity, social network analysis, eCommerce recommendations, dependence analysis, and predictive analytics.”

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