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.

Read the full interview at AIThority.

AllegroGraph with FedShard driving growth for Entity-Event Knowledge Graph Solutions

Franz Inc. has been named to Database Trends and Applications – 100 Companies that Mater Most in Data. Underscoring Franz’s technology leadership in Data Management, the company’s Knowledge Graph Platform, AllegroGraph, was relied upon for market research in leading analyst reports, including Forrester’s Now Tech: Multimodel Data Platforms, Q1 2021 and the Gartner Case Study: Entity-Event Knowledge Graph for Powering AI Solutions (Montefiore).

“The past 15 months have provided an object lesson for all organizations in the importance of being agile and moving swiftly to address new challenges and opportunities. If companies were not dealing with enough already with burgeoning data volumes, expanding regulatory mandates, and new data security concerns, the ongoing pandemic added to the pressure and created heightened urgency for real-time, data-driven insights as well as more ubiquitous data access,” stated DBTA Group Publisher Tom Hogan, Jr. “Spanning a spectrum of approaches, the DBTA 100 showcases forward-looking companies that are improving and expanding upon existing technologies and processes to help their customers use data more effectively.”

“Franz Inc. has a rich, innovative history and we are honored to receive this acknowledgement for our efforts in delivering scalable Knowledge Graph Solutions,” said Dr. Jans Aasman, CEO, Franz Inc. “In the past year, we have seen 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 Enterprise-Wide Data Fabrics.   AllegroGraph 7 with FedShard, uniquely provides companies with the foundational environment for delivering Data Fabric solutions with the ability to continually enrich and contextualize the understanding of data.”

Read more about Franz Inc. and the Award.

By Dr. Jans Aasman, CEO, Franz Inc.

Today organizations capture an enormous amount of information in spoken conversations, from routine customer service calls to sophisticated claims processing interactions in finance and healthcare. But most of this information remains hidden and unused due to the difficulty of turning these conversations into meaningful data that can be effectively analyzed through Natural Language Processing (NLP).

Simply applying speech recognition software to voice conversations often results in unreliable data. State-of-the-art speech recognition systems still have trouble distinguishing between homophones (words with the same pronunciation, but different meanings), as well as the difference between proper names (i.e. people, products) and separate words. In addition, there is also the challenge of identifying domain-specific words accurately. Thus, in most cases, using speech recognition software alone doesn’t produce accurate enough data for reliable NLP.

Domain-specific taxonomies are key to understanding conversations via speech recognition systems. With them, we can feed conversations to knowledge graphs that understand the conversation and make connections in the data. Knowledge graphs provide the ability to extract the correct meaning of text from conversations and connect concepts in order to add business value.

Knowledge graphs fed with NLP provide two prime opportunities for monetization. First, organizations can better understand their customers to improve products and services more to their liking, which in turn boosts marketing, sales and customer retention rates. Secondly, this analysis gives contact center agents real-time support for optimizing customer interactions to produce faster resolutions, better conversion rates, and cross-selling and up-selling opportunities. These approaches enable companies to capitalize on speech recognition knowledge graphs, accelerate their ROI, and expand their bottom lines.

Taxonomy Driven Speech Recognition

The story of taxonomy-driven speech recognition closely relates to knowledge graphs. The first wave of knowledge graphs was built from taking structured data and turning it into semantic graphs that support the linked open data movement. The next wave is all about unstructured data. People started doing Natural Language Processing on documents and textual conversations like emails and chats. Doing so accurately for a given domain requires a taxonomy to understand the words and concepts. Otherwise, downstream processes like entity extraction and event detection won’t work.

Read the full article at DZone.

Bringing knowledge graph and machine learning technology together can improve the accuracy of the outcomes and augment the potential of machine learning approaches. With knowledge graphs, AI language models are able to represent the relationships and accurate meaning of data instead of simply generating words based on patterns.

Read this special report to dive into key uses cases, best practices for getting started, and technology solutions every organization should know about.

The Future of AI: Machine Learning and Knowledge Graphs

Gartner featured Franz’s customer, Montefiore Medical Center, in a research report on Montefiore’s Entity-Event Knowledge Graph:

“AI solutions are often hindered by fragmented data and siloed point solutions,” according to Gartner’s Chief Data and Analytics Officer Research Team. “Montefiore’s data and analytics leader used semantic knowledge graphs to power its AI solutions and achieved considerable cost savings as well as improvements in timeliness and the prediction accuracy of AI models.” Source: Gartner Case Study: Entity-Event Knowledge Graph for Powering AI Solutions (Montefiore) – Subscription required.

Copy Available from Montefiore/Einstein.

Franz Inc., is proud to announce that it has been named to The 100 Companies That Matter in Knowledge Management by KMWorld. The annual list reflects the urgency felt among many organizations to provide a timely flow of targeted information. Among the more prominent initiatives is the use of AI and cognitive computing, as well as related capabilities such as machine learning, natural language processing, and text analytics.

“Flexibility, agility, and the ability to pivot are attributes that have become critical to forward-thinking companies—and that is particularly the case now. Successful organizations don’t want to merely survive; they want to dominate their market sectors. But to do that, they need the right tools and products,” said Tom Hogan, Group Publisher at KMWorld. “Amidst the dramatic changes taking place today, innovative organizations are seeking new approaches to improve their processes. The 2021 KMWorld 100 is a list of leading-edge knowledge management companies that are helping their customers to expand access to information, leverage new opportunities, and accelerate growth.”

Read More about Franz Inc.

Data and the subsequent knowledge derived from information are the most valuable strategic asset an organization possesses. Despite the abundance of sophisticated technology developments, most organizations don’t have disciplines or a plan to enable data-centric principles.

DCAF 2021 will help provide clarity.

Our overarching theme for this conference is to make it REAL. Real in the sense that others are becoming data-centric, it is achievable, and you are not alone in your efforts.

Join us in understanding how data as an open, centralized resource outlives any application. Once globally integrated by sharing a common meaning, internal and external data can be readily integrated, unlike the traditional “application-centric” mindset predominantly used in systems development.

The compounding problem is these application systems each have their own completely idiosyncratic data models. The net result is that after a few decades, hundreds or thousands of applications implemented have given origin to a segregated family of disparate data silos. Integration debt rises and unsustainable architectural complexity abounds with every application bought, developed, or rented (SaaS).

Becoming data-centric will improve data characteristics of findability, accessibility, interoperability, and re-usability (FAIR principles), thereby allowing data to be exported into any needed format with virtually free integration.\

Dr. Jans Aasman to present – Franz’s approach to Entity Event Data Modeling for Enterprise Knowledge Fabrics

Dr. Jans Aasman’s recent article in Dzone.

The data fabric notion is gaining credence throughout the analyst community, in much the same way knowledge graphs have done so for years. Both technologies link all relevant data for a specific business purpose, which is why the most successful companies in the world employ them.

Amazon’s knowledge graph retains metadata about its vast product array; Google’s captures data about an exhaustive list of web entities of interest. Lesser-known organizations regularly deploy these mechanisms for everything from comprehensive customer views to manufacturing processes.

Data fabrics have a unique, symbiotic relationship with the knowledge graph movement because they substantially streamline the processes to extract data from the myriad sources that populate these platforms. In turn, knowledge graphs provide some of the fundamental capabilities enabling data fabrics to accomplish this objective.

Read the Full Article at Dzone.

The inaugural episode of The Graph Show, featured Josh Shinavier, Research Scientist at Uber, interviewing Franz Inc.’s CEO, Dr. Jans Aasman.

For more info on The Graph Show, visit:
http://thegraphshow.com

International Ontology Sharing Is Becoming A Reality

A consortium of researchers recently formed an organization dedicated to standardizing how scientists define their ontologies, which are essential for retrieving datasets as well as understanding and reproducing research. The group called OntoPortal Alliance is creating a public repository of internationally shared domain-specific ontologies. All the repositories will be managed with a common OntoPortal appliance that has been tested with AllegroGraph Semantic Knowledge Graph software. This enables any OntoPortal adopter to get all the power, features, maintainability, and support benefits that come from using a widely adopted, state-of-the-art semantic knowledge graph database.

Read the full article at HealthIT Outcomes –

As Dr. Jans Aasman, CEO of Franz Inc. explains, “When building a Knowledge Graph as your enterprise’s single source of truth, it’s critical to include ontologies and taxonomies. AI applications and complex reasoning analytics require information from both databases and knowledge bases that contain domain information, taxonomies, and ontologies to solve complex questions. To make this possible, we developed a novel hybrid sharding technology called FedShard, which facilitates the combination of data and knowledge required by applications like Montefiore’s PALM. But this approach is not unique or specific to Healthcare, it is applicable in many other industries, which is why we are excited about OntoPortal’s plans to bring sharing of domain ontologies to a broad audience.”