AiThority Interview with Dr. Jans Aasman, CEO at Franz

Jans Aasman, please tell us about your current role and the team / technology you handle at Franz.

As CEO of Franz Inc., I drive the overall technology vision for our Enterprise Knowledge Graph solutions and ensure our customer projects deliver the ROI results expected with graph based architectures.

Franz Inc. is composed of an expert team with skills in Graph Databases, Semantic technologies, Graph Visualization, AI, NLP and Machine Learning.  Our domain knowledge encompasses large enterprises in Healthcare, Pharma, Customer Support, and Intelligence Agencies.

Our main business today revolves around AllegroGraph, a Semantic Graph platform that allows infinite data integration through a patented approach unifying all data and siloed knowledge into an Entity-Event Knowledge Graph solution that can support massive big data analytics. AllegroGraph’s FedShard feature utilizes patented federated sharding capabilities that drive 360-degree insights and enable complex reasoning across a distributed Knowledge Graph. AllegroGraph is utilized by dozens of the top Fortune 500 companies worldwide.

We also offer a popular data visualization and no-code query builder called Gruff – the most advanced Knowledge Graph visualization application on the market, which we recently integrated into Franz AllegroGraph. Gruff enables users to create visual Knowledge Graphs that display data relationships in views that are driven by the user. Ad hoc and exploratory analysis can be performed by simply clicking on different graph nodes to answer questions. Gruff’s unique ‘Time Machine’ feature provides the capability to explore temporal context and connections within data. The visual query builder within Gruff empowers both novice and expert users to create simple to highly complex queries without writing any code.

The Franz team has unique expertise in designing ontology and taxonomy-based solutions by utilizing standards-based development processes and tools. We also offer data integration services from siloed data using W3C industry standard semantics, which can then be continually integrated with information that comes from other data sources. In addition, our data science team provides expertise in custom algorithms to maximize data analytics and uncover hidden knowledge.

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How has the world of Knowledge Graphs evolved in the last 2-3 years?

In the past 5 years, Graph technology has gone from being used for speciality analytics to mainstream. Knowledge Graphs have become the fundamental framework for AI applications. The proliferation of Artificial Intelligence has resulted in the need for increasingly complex queries over more data. AllegroGraph-powered Knowledge Graphs addresses two of the most daunting challenges in AI continuous data integration and the ability to query across all available data.

With AllegroGraph, organizations can create Data Fabrics underpinned by AI Knowledge Graphs that take advantage of the infinite data integration capabilities possible with our FedShard technology and the ability to query across all the data – both sharded and unsharded – delivering holistic insights instantaneously.

Gartner also believes in the power of graphs. Gartner’s Top 10 Trends in Data and Analytics for 2020 noted, “Relationships form the foundation of data and analytics value.

By 2023, graph technologies will facilitate rapid contextualization for decision making in 30% of organizations worldwide. Graph analytics is a set of analytic techniques that allows for the exploration of relationships between entities of interest such as organizations, people and transactions. Data and analytics leaders need to evaluate opportunities to incorporate graph analytics into their analytics portfolios and applications to uncover hidden patterns and relationships. In addition, consider investigating how graph algorithms and technologies can improve your AI and ML initiatives.” (Source: Gartner, Top 10 Trends in Data and Analytics for 2020, June 9, 2020).

What influence does the Open Source community have on building powerful AI solutions?

It seems that nearly all data science progress happens in the Open Source community. And the surprising phenomenon is that it comes from two totally different directions.

On the one hand, we see tens of thousands of PhD students in the academic world publish new methods and algorithms for deep learning, graph neural networks, natural language processing, vision, robotics, etc. And obviously, that is all mostly open source. No regular company can compete with that amount of brain power. On the other hand, you have really big companies like Facebook, Google, Twitter and others that actually can compete when it comes to brain power, but the surprising thing is that they will regularly put their new AI techniques back in the open source, or at least make it available as free models or affordable services.

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Tell us more about your partnership with Smartlogic. What kind of integrations are you developing to boost the adoption of Knowledge Graph solutions?

Franz’s AllegroGraph with FedShard and Smartlogic’s Semaphore are both designed for the world’s largest enterprises that demand scale and quality. The unique combination of our technology draws upon the power of Semantic AI with award-winning knowledge model management – allowing organizations to capitalize on information across governance and compliance, provide customer 360 views, and gain valuable insights from previously unavailable information to drive new revenue streams, gain organizational efficiencies, and achieve cost savings.

What is the most contemporary definition of Semantic AI? How would this change with your partnership with Smartlogic?

Semantic AI today includes the ability to combine complex data and knowledge from diverse sources and connect with Knowledge Graphs into a Data Fabric capable of understanding the holistic context, subtle differences and nuances of data over time – and delivering real-time predictions and insights. Insights can be derived from using Machine Learning, Graph Neural Networks, Natural Language Processing, etc.

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Hear it from the pro: Can you share the top hiring trends that you are expecting in your industry? How do you train your AI team in practical applications?

The explosion in demand for Knowledge Graph solutions is driving the need for more  Knowledge Engineers, Data Fabric Architects, AI Engineers with skills in building ontologies and taxonomies.

The emerging trend of Graph Neural Networks will require engineers that have a deep understanding of both graph technology and machine learning. Uptill a few years ago universities embraced this silly notion that modern AI=ML but now we see the new trend that students are also engaging in graph studies. Because the real power of AI is in the combination of both.

Thank you, Jans! That was fun and we hope to see you back on AiThority.com soon.