Venture Beat Features Montefiore’s Healthcare project with AllegroGraph

From VentureBeat August 2018

This article discusses Montefiore’s PALM project that uses AllegroGraph:

Montefiore is one of the largest employers in New York State. It’s also one of the busiest health care complexes — hundreds of thousands of patients pass through its sprawling campuses, which include Montefiore Medical Center, the Albert Einstein College of Medicine, and Montefiore Medical Park.

Those logistical challenges catalyzed the development of Montefiore’s Patient-centered Analytical Learning Machine (PALM), a machine learning platform built from the ground up to predict and prevent life-threatening medical conditions and minimize wait times.

PALM juggles lots of datasets — electronic medical records, insurance billing codes, drug databases, and clinical trial results, to name a few. And its analytical models recently expanded to handle voice, images, and sensor inputs from internet of things devices.

Core to the semantic graph model are triplestores, which are a type of database optimized for filing away and retrieving triples. They’re an entity composed of subject-predicate-object — “John has tuberculosis,” for example — which PALM builds dynamically, as needed. Along the way, the system uses a frame data language, or FDL, to resolve ambiguities, like when some electronic records refer to medication by its brand instead of by its scientific name (e.g., “Advil” or “Motrin” instead of ibuprofen).

Read the full article over at Venture Beat.

 




Optimizing Fraud Management with AI Knowledge Graphs

From Global Banking and Finance Review – July 12, 2018

This article discusses Knowledge Graphs for Anti-Money Laundering (AML), Suspicious Activity Reports (SAR), counterfeiting and social engineering falsities, as well as synthetic, first-party, and card-not-present fraud.

By compiling fraud-related data into an AI knowledge graph, risk management personnel can also triage those alerts for the right action at the right time. They also get the additive benefit of reusing this graph to decrease other risks for security, loans, or additional financial purposes.

Dr. Aasman goes on to note:

By incorporating AI, these threat maps yields a plethora of information for actually preventing fraud. Supervised learning methods can readily identify what events constitute fraud and which don’t; many of these involve classic machine learning.  Unsupervised learning capabilities are influential in determining normal user behavior then pinpointing anomalies contributing to fraud. Perhaps the most effective way AI underpins risk management knowledge graphs is in predicting the likelihood—and when—a specific fraud instance will take place. Once organizations have data for customers, events, and fraud types over a length of time (which could be in as little as a month in the rapidly evolving financial crimes space), they can compute the co-occurrence between events and fraud types.

Read the full article over at Global Banking and Finance Review.

 




The Cornerstone of Data Science: Progressive Data Modeling

From AI Business June 27, 2018

This article covers Single Schema, Universal Taxonomies, Time Series Analysis, Accelerating Data Science and features some thought leadership from Franz Inc.’s CEO, Jans Aasman:

‘Contemporary data science and artificial intelligence requirements simply can’t wait for this ongoing, dilatory process. According to Jans Aasman, CEO of Franz, they no longer have to. By deploying what Aasman called an “events-based approach to schema”, companies can model datasets with any number of differences alongside one another for expedited enterprise value.’

‘The resulting schema is simplified, uniform, and useful in multiple ways. “You achieve two goals,” Aasman noted. “One is you define what data you trust to be in the main repository to have all the truth. The second thing is you make your data management a little more uniform. By doing those two things your AI and your data science will become better, because the data that goes into them is better.”’

Dr. Aasman goes on to note:

‘The events-based schema methodology only works with enterprise taxonomies—or at least with taxonomies spanning the different sources included in a specific repository, such as a Master Data Management hub. Taxonomies are necessary so that “the type of event can be specified,” Aasman said.’

‘Moreover, taxonomies are indispensable for clarifying terms and their meaning across different data formats, which may represent similar concepts in distinct ways. Therefore, practically all objects in a database should be “taxonomy based” Aasman said, because these hierarchical classifications enable organizations to query their repositories via this uniform schema.’

Read the full article over at AI Business.




New York Times Article – Is There a Smarter Path to Artificial Intelligence?

From the New York Times – June 20, 2018

This article caught our attention because they featured a startup that was using Prolog for AI.   We have been strong proponents of Prolog for Semantic Graph solutions for many years.

For the past five years, the hottest thing in artificial intelligence has been a branch known as deep learning. The grandly named statistical technique, put simply, gives computers a way to learn by processing vast amounts of data. Thanks to deep learning, computers can easily identify faces and recognize spoken words, making other forms of humanlike intelligence suddenly seem within reach.

Companies like Google, Facebook and Microsoft have poured money into deep learning. Start-ups pursuing everything from cancer cures to back-office automation trumpet their deep learning expertise. And the technology’s perception and pattern-matching abilities are being applied to improve progress in fields such as drug discovery and self-driving cars.

But now some scientists are asking whether deep learning is really so deep after all……

………Those other, non-deep learning tools are often old techniques employed in new ways. At Kyndi, a Silicon Valley start-up, computer scientists are writing code in Prolog, a programming language that dates to the 1970s. It was designed for the reasoning and knowledge representation side of A.I., which processes facts and concepts, and tries to complete tasks that are not always well defined. Deep learning comes from the statistical side of A.I. known as machine learning.

Our Tweet with links to AllegroGraph Prolog documenation and the full article:

“computer scientists are writing code in … It was designed for the reasoning and knowledge representation side of ….” is the only to include for your AI apps.




How AI Boosts Human Expertise at Wolters Kluwer

Wolters Kluwer, a long time AllegroGraph customer, recently spoke with Alex Woodie at Datanami to describe how they are using AI tools such at AllegroGraph:

Thousands of companies around the world rely on Wolters Kluwer’s practice management software to automate core aspects of their businesses. That includes doctor’s offices that use its software make healthcare decisions in a clinical setting, corporate law offices that use its software to understand M&A activities, and accounting firms that use its software to craft tax strategies for high net-worth clients.

Wolters Kluwer is embedding a range of AI capabilities – including deep learning and graph analytics — across multiple product lines. For example, its Legalview Bill Analyzer software helps to identify errors in legal bills sent from outside law firms to the corporate counsels of large companies. The typical recovery rate for people reviewing bills manually is 1% to 2%. By adding machine learning technology to the product the recovery rate jumps to 7% to 8%, which can translate into tens of millions of dollars.

Wolters Kluwer is using graph analytic techniques to accelerate the knowledge discovery process for its clients across various professions. The company has tapped Franz‘s AllegroGraph software to help it drive new navigational tools for helping customers find answers to their questions.

By arranging known facts and concepts as triples in the AllegroGraph database and then exposing those structures to users through a traditional search engine dialog box, Wolters Kluwer is able to surface related insights in a much more interactive manner.

“We’re providing this live feedback. As you’re typing, we’re providing question and suggestions for you live,” Tatham said. “AllegroGraph gives us a performant way to be able to just work our way through the whole knowledge model and come up with suggestion to the user in real time.”

Read the full article over at Datanami.