The most interesting use is the most powerful: standard RDF graphs for large-scale knowledge graph integration.

From my notes on a talk Parsa Mirhaji of Montefiore Health System gave in 2017. Montefiore uses Franz AllegroGraph, a distributed RDF graph database. He modeled a core patient-centric hospital knowledge need using a simple standard ontology with a 1,000 or so concepts total.

That model integrated data from lots of different kinds of heterogeneous sources so that doctors could query the knowledge graph from tablets or phones at a patient’s bedside and get contextualized, patient-specific answers to questions for diagnostic purposes.

Fast forward to 2018, and nine out of ten of the most value-creating companies in the world are using standard knowledge graphs in a comparable fashion, either as a base for multi-domain intelligent assistants a la Siri or Alibot or Alexa, or to integrate and contextualize business domains cross-enterprise, or both. The method is preparatory to what John Launchbury of DARPA described as the Third Wave of AI………….

This article discusses the facets of machine learning and AI:

Lauded primarily for its automation and decision support, machine learning is undoubtedly a vital component of artificial intelligence. However, a small but growing number of thought leaders throughout the industry are acknowledging that the breadth of AI’s upper cognitive capabilities involves more than just machine learning.

Machine learning is all about sophisticated pattern recognition. It’s virtually unsurpassable at determining relevant, predictive outputs from a series of data-driven inputs. Nevertheless, there is a plethora of everyday, practical business problems that cannot be solved with input/output reasoning alone. The problems also require the multistep, symbolic reasoning of rules-based systems.

Whereas machine learning is rooted in a statistical approach, symbolic reasoning is predicated on the symbolic representation of a problem usually rooted in a knowledge base. Most rules-based systems involve multistep reasoning, including those powered by coding languages such as Prolog.

Digitalized Ecosystems

Emerging technologies require revolutionizing the enabling foundations that provide the volume of data needed, advanced compute power and ubiquity-enabling ecosystems. The shift from compartmentalized technical infrastructure to ecosystem-enabling platforms is laying the foundations for entirely new business models that are forming the bridge between humans and technology.

This trend is enabled by the following technologies: Blockchain, Blockchain for Data Security, Digital Twin, IoT Platform and Knowledge Graphs.

“Digitalized ecosystem technologies are making their way to the Hype Cycle fast,” said Walker. “Blockchain and IoT platforms have crossed the peak by now, and we believe that they will reach maturity in the next five to 10 years, with digital twins and knowledge graphs on their heels.”

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).

This article covers machine learning and AI:

According to Franz CEO Jans Aasman, these machine learning deployments not only maximize organizational investments in them by driving business value, but also optimize the most prominent aspects of the data systems supporting them.

“You start with the raw data…do analytics on it, get interesting results, then you put the results of the machine learning back in the database, and suddenly you have a far more powerful database,” Aasman said.

Dr. Aasman is further quoted:

For internal applications, organizations can use machine learning concepts (such as co-occurrence—how often defined concepts occur together) alongside other analytics to monitor employee behavior, efficiency, and success with customers or certain types of customers. Aasman mentioned a project management use case for a consultancy company in which these analytics were used to “compute for every person, or every combination of persons, whether or not the project was successful: meaning, done on time to the satisfaction of the customer.”

Organizations can use whichever metrics are relevant for their businesses to qualify success. This approach is useful for determining a numerical rating for employees “and you could put that rating back in the database,” Aasman said. “Now you can do a follow up query where you say how much money did I make on the top 10 successful people; how much money did I lose on the top 10 people I don’t make a profit on.”

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.”’