Dr. Jans Aasman Presented
Why Knowledge Graphs Hit the Hype Cycle and What they have in common.
The Ontolog Forum is an open, international, virtual community of practice devoted to advancing the field of ontology, ontological engineering and semantic technology, and advocating their adoption into mainstream applications and international standards.
Franz’s CEO, Jans Aasman’s recent IoT Evolution Article:
Conceptually, the promise of the Internet of Things is almost halcyon. Its billions of sensors are all connected, continuously transmitting data to support tailored, cost-saving measures maximizing revenues in applications as diverse as smart cities, smart price tags, and predictive maintenance in the Industrial Internet.
Practically, the data management necessities of capitalizing on this promise by the outset of the next decade are daunting. The vast majority of these datasets are unstructured or semi-structured. The data modeling challenges of rectifying their schema for integration are considerable. The low latency action required to benefit from their data implies machine intelligence largely elusive to today’s organizations.
The self-describing, linked data approach upon which JSON-LD is founded excels at the low latent action resulting from machine to machine communication in the IoT. The nucleus of the linked data methodology—semantic statements and their unique Uniform Resource Identifiers (URIs)—are read and understood by machines. This characteristic aids many of the IoT use cases requiring machine intelligence; by transmitting IoT data via the JSON-LD format organizations can maximize this boon. Smart cities provide particularly compelling examples of the machine intelligence fortified by this expression of semantic technology.
Read the full article at IoT Evolution
Franz’s CEO, Jans Aasman’s recent Destination CRM article:
The need for text analytics and speech recognition has broadened over the years, becoming more prevalent and essential in the sales, marketing, and customer service departments of various types of businesses and industries. The goal is simple for these contact center use cases: provide real-time assistance to human agents interacting with potential customers to close sales, initiate them, and increase customer satisfaction.
Until fairly recently, the rich array of unstructured data encompassing client texts, chats, and phone calls was obscured from contact centers and organizations due to the sheer arduousness of speech recognition and text analytics. When readily integrated into knowledge graphs, however, these same sources become some of the most credible for improving agent interactions and achieving business objectives.
Powered by the shrewd usage of organizational taxonomies, machine learning, natural language processing (NLP), and semantic search, knowledge graphs make speech recognition and text analytics immediately accessible, enabling real-time customer interactions that can maximize business objectives—and revenues.
Taxonomies are the foundation of the knowledge graph approach to rapidly conveying results of speech recognition and text analytics for timely customer interactions. Agents need three types of information to optimize customer interactions: their personas (such as an executive or a purchase department representative, for example), their reasons for contacting them, and their industries. Taxonomies are instrumental to performing these functions because they provide a hierarchy of relevant terms to organizations.
Read the full article at Destination CRM
A linked data framework can empower smart cities to realize social, political, and financial goals.
Smart cities are projected to become one of the most prominent manifestations of the Internet of Things (IoT). Current estimates for the emerging smart city market exceed $40 trillion, and San Jose, Barcelona, Singapore, and many other major metropolises are adopting smart technologies.
The appeal of smart cities is binary. On the one hand, the automated connectivity of the IoT is instrumental in reducing costs associated with public expenditures for infrastructure such as street lighting and transportation. With smart lighting, municipalities only pay for street light expenses when people are present. Additionally, by leveraging options for dynamic pricing with smart parking, for example, the technology can provide new revenue opportunities.
Despite these advantages, smart cities demand extensive data management. Consistent data integration from multiple locations and departments is necessary to enable interoperability between new and legacy systems. Smart cities need granular data governance for long-term sustainability. Finally, they necessitate open standards to future-proof their perpetual utility.
Knowledge graphs—enterprise-wide graphs which link all data assets for internal or external use—offer all these benefits and more. They deliver a uniform, linked framework for sharing data in accordance with governance protocols, are based on open standards, and exploit relationships between data for business and operational optimization. They supply everything smart cities need to realize their social, political, and financial goals. Knowledge graphs can use machine learning to reinsert the output of contextualized analytics into the technology stack, transforming the IoT’s copious data into foundational knowledge to spur improved civic applications.
Read the full article at Trajectory Magazine
Franz’s CEO, Jans Aasman’s recent Forbes article:
There’s a fascinating dichotomy in artificial intelligence between statistics and rules, machine learning and expert systems. Newcomers to artificial intelligence (AI) regard machine learning as innately superior to brittle rules-based systems, while the history of this field reveals both rules and probabilistic learning are integral components of AI.
This fact is perhaps nowhere truer than in establishing explainable AI, which is central to the long-term business value of AI front-office use cases.
Granted, simple machine learning can automate backend processes. However, the full extent of deep learning or complex neural networks — which are much more accurate than basic machine learning — for mission-critical decision-making and action requires explainability.
Using rules (and rules-based systems) to explicate machine learning results creates explainable AI. Many of the far-reaching applications of AI at the enterprise level — deploying it to combat financial crimes, to predict an individual’s immediate and long-term future in health care, for example — require explainable AI that’s fair, transparent and regulatory compliant.
Rules can explain machine learning results for these purposes and others.
Read the full article at Forbes
Presenters – Burt Smith, N3 Results and Jans Aasman, Franz Inc.
In the typical sales organization the contents of the actual chat or voice conversation between agent and customer is a black hole. In the modern Intelligent Customer Operations center (e.g. N3 Results – www.n3results.com) the interactions between agent and customer are a source of rich information that helps agents to improve the quality of the interaction in real time, creates more sales, and provides far better analytics for management.
Join us for this Webinar where we describe a real world Intelligent Customer Operations center that uses graph based technology for taxonomy driven entity extraction, speech recognition, machine learning and predictive analytics to improve quality of conversations, increase sales and improve business visibility.
View the recorded webinar.
Algorithm-XLab – March 2019
Franz CEO Dr. Jans Aasman Explains how to manage AI Modelling Risks.
AI model risk management has moved to the forefront of contemporary concerns for statistical Artificial Intelligence, perhaps even displacing the notion of ethics in this regard because of the immediate, undesirable repercussions of tenuous machine learning and deep learning models.
AI model risk management requires taking steps to ensure that the models used in artificial applications produce results that are unbiased, equitable, and repeatable.
The objective is to ensure that given the same inputs, they produce the same outputs.
If organizations cannot prove how they got the results of AI risk models, or have results that are discriminatory, they are subject to regulatory scrutiny and penalties.
Strict regulations throughout the financial services industry in the United Statesand Europe require governing, validating, re-validating, and demonstrating the transparency of models for financial products.
There’s a growing cry for these standards in other heavily regulated industries such as healthcare, while the burgeoning Fair, Accountable, Transparent movementtypifies the horizontal demand to account for machine learning models’ results.
AI model risk management is particularly critical in finance.
Financial organizations must be able to demonstrate how they derived the offering of any financial product or service for specific customers.
When deploying AI risk models for these purposes, they must ensure they can explain (to customers and regulators) the results that determined those offers.
Read the full article at Algorithm-XLab.
Download – AllegroGraph v6.5 and Gruff v7.3
AllegroGraph – Documentation
Gruff – Documentation
Adding JSON/JSON-LD Documents to a Graph Database
Traditional document databases (e.g. MongoDB) have excelled at storing documents at scale, but are not designed for linking data to other documents in the same database or in different databases. AllegroGraph 6.5 delivers the unique power to define many different types of documents that can all point to each other using standards-based semantic linking and then run SPARQL queries, conduct graph searches, execute complex joins and even apply Prolog AI rules directly on a diverse sea of objects.
AllegroGraph 6.5 provides free text indexes of JSON documents for retrieval of information about entities, similar to document databases. But unlike document databases, which only link data objects within documents in a single database, AllegroGraph 6.5 moves the needle forward in data analytics by semantically linking data objects across multiple JSON document stores, RDF databases and CSV files. Users can run a single SPARQL query that results in a combination of structured data and unstructured information inside documents and CSV files. AllegroGraph 6.5 also enables retrieval of entire documents.
There are many reasons for working with JSON-LD. The big search engines force ecommerce companies to mark up their webpages with a systematic description of their products and more and more companies use it as an easy serialization format to share data.
A direct benefit for companies using AllegroGraph is that they now can combine their documents with graphs, graph search and graph algorithms. Normally when you store documents in a document database you set up your documents in such a way that it is optimized for certain direct retrieval queries. Performing complex joins for multiple types of documents or even performing a shortest path through a mass of object (types) is too complicated. Storing JSON-LD objects in AllegroGraph gives users all the benefits of a document database AND the ability to semantically link objects together, run complex joins, and perform graph search queries.
Another key benefit for companies is that your application developers don’t have to learn the entire semantic technology stack, especially the part where developers have to create individual RDF triples or edges. Application developers love to work with JSON data as serialization for objects. In JavaScript the JSON format is syntactically identical to the code for creating JavaScript objects and in Python the most import data structure is the ‘dictionary’ which is also near identical to JSON.
Key AllegroGraph v6.5 Features:
Numerous other optimizations, features, and enhancements. Read the release notes – https://franz.com/agraph/support/documentation/current/release-notes.html
_linkedin_partner_id = "958892";
window._linkedin_data_partner_ids = window._linkedin_data_partner_ids || [];
window._linkedin_data_partner_ids.push(_linkedin_partner_id);
(function(){var s = document.getElementsByTagName("script")[0];
var b = document.createElement("script");
b.type = "text/javascript";b.async = true;
b.src = "https://snap.licdn.com/li.lms-analytics/insight.min.js";
s.parentNode.insertBefore(b, s);})();
<br />
<img height="1" width="1" style="display:none;” alt=”” src=”https://dc.ads.linkedin.com/collect/?pid=958892&fmt=gif” />
Forbes – February 2019
Although you may not have heard of JavaScript Object Notation Linked Data (JSON-LD), it is already affecting your business. Search engine giant Google has mentioned JSON-LD as a preferred means of adding structured data to webpages to make them considerably easier to parse for more accurate search engine results. The Google use case is indicative of the larger capacity for JSON-LD to increase web traffic for sites and better guide users to the results they want.
Expectations are high for JSON-LD, and with good reason. It effectively delivers the many benefits of JSON, a lightweight data interchange format, into the linked data world. Linked data is the technological approach supporting the World Wide Web and one of the most effective means of sharing data ever devised.
In addition, the growing number of enterprise knowledge graphs fully exploit the potential of JSON-LD as it enables organizations to readily access data stored in document formats and a variety of semi-structured and unstructured data as well. By using this technology to link internal and external data, knowledge graphs exemplify the linked data approach underpinning the growing adoption of JSON-LD — and the demonstrable, recurring business value that linked data consistently provides.
Read the full article at Forbes.
Crypto Slate – January 2019
by Dr. Jans Aasman, CEO, Franz Inc.
If organizations could only augment blockchain’s strengths—its immutability, security, and decentralization—while addressing its latency and scalability issues, it could become the vaunted enterprise tool it was initially intended. That day will soon come courtesy of Directed Acyclic Graphs (DAGs).
Blockchain’s premise is straightforward, utilitarian, and more lucrative than that of any other new technology to recently emerge. This distributed ledger system promises near real-time updates of transactions between remote parties for trustworthy, impenetrable peer-to-peer networks, eliminating the need (and expense) of middlemen.
Read the full article at Crypto Slate.