Forbes published the following article by Dr. Jans Aasman, Franz Inc.’s CEO.

Although there has been great progress in artificial intelligence (AI) over the past few years, many of us remember the AI winter in the 1990s, which resulted from overinflated promises by developers and unnaturally high expectations from end users. Now, industry insiders, such as Facebook head of AI Jerome Pesenti, are predicting that AI will soon hit another wall—this time due to the lack of semantic understanding.

“Deep learning and current AI, if you are really honest, has a lot of limitations,” said Pesenti. “We are very, very far from human intelligence, and there are some criticisms that are valid: It can propagate human biases, it’s not easy to explain, it doesn’t have common sense, it’s more on the level of pattern matching than robust semantic understanding.”

Read the full article at Forbes.

Northern California’s proactive power outages were not necessary last fall. Digital Twin technology can predict utility line failures and turn off power in milliseconds to avoid the potential of sparks igniting the surrounding area.

Digital twin technologies are gaining traction across industries and use cases. Initially devised as a means of monitoring assets and production settings in manufacturing, this technology has quietly seeped into other verticals like hospitality, construction, and building management and soon, electricity delivery.

The premier problem digital twins will solve is predicting power grid failure, which would alleviate the social, economic, and political issues that resulted from efforts to reduce the incidence and degree of catastrophes, property loss, and deaths stemming from downstream effects of power grid failure—such as recurring wildfires.

Digital twins can allay these concerns because they’re based on real-time signals from a comprehensive set of factors that could be indicative of power grid woes related to environmental, meteorological, or technology concerns. Moreover, they can deliver accurate predictions for each of these factors well in advance of failure—in some cases as much as 28 days.

Read the full article at PowerGrid International.

Dr. Jans Aasman, CEO, Franz Inc., will be presenting, “Creating Explainable AI with Rules” at the Global Graph Summit, a part of Data Day Texas. The abstract for Dr. Aasman’s presentation:

“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.”

“The fundamental necessity for explainable AI spans regulatory compliance, fairness, transparency, ethics and lack of bias — although this is not a complete list. For example, the effectiveness of counteracting financial crimes and increasing revenues from advanced machine learning predictions in financial services could be greatly enhanced by deploying more accurate deep learning models. But all of this would be arduous to explain to regulators. Translating those results into explainable rules is the basis for more widespread AI deployments producing a more meaningful impact on society.”

The Global Graph Summit is an independently organized vendor-neutral conference,  bringing leaders from every corner of the graph and linked-data community for sessions, workshops, and its well-known before and after parties.  Originally launched in January 2011 as one of the first NoSQL / Big Data conferences, Data Day Texas each year highlights the latest tools, techniques, and projects in the data space, bringing speakers and attendees from around the world to enjoy the hospitality that is uniquely Austin. Since its inception, Data Day Texas has continually been the largest independent data-centric event held within 1000 miles of Texas.

For more examples visit – https://github.com/franzinc/agraph-examples

Introduction
This example demonstrates an RDF model for Bitcoin chain data as well as a Python tool to pull the data from a Bitcoin node into an instance of an AllegroGraph graph database. The model description itself can be found in the Turtle file model.ttl.

The following Turtle example demonstrates how this RDF model can be used to represent complete chain entities (given example is a genesis block – the first block in the mainnet Bitcoin chain; script strings omitted for brevity):

@prefix : <https://raw.githubusercontent.com/franzinc/agraph-examples/master/data/bitcoin/model.ttl#>
@prefix btc: <bitcoin://>

btc:blk0
:height 0;
:hash “000000000019d6689c085ae165831e934ff763ae46a2a6c172b3f1b60a8ce26f”;
:time 1231006505;
:version 1;
:transaction btc:4a5e1e4baab89f3a32518a88c31bc87f618f76673e2cc77ab2127b7afdeda33b.

btc:4a5e1e4baab89f3a32518a88c31bc87f618f76673e2cc77ab2127b7afdeda33b
:lockTime 0;
:input [:unlockScript “…”.];
:output [:amount 5000000000; :lockScript “…”.].

Setup
The following examples assume AllegroGraph triple store and assume it is already installed and running on the target machine. The following AG instance settings settings are assumed as well:

host: localhost (default);
port: 10035 (default);
username: aguser;
password: agpassword.

We also assume the following bitcoind settings:

host: localhost (default);
port: 8332 (default);
username: btcuser;
password: btcpassword.

First, install the tool by cloning this repository, setting up virtual environment and installing the dependencies:

git clone http://github.com/franzinc/agraph-examples
cd agraph-examples/data/bitcoin
python3 -m venv .
source ./bin/activate
pip3 install -r requirements.txt

The following command starts the process of loading bitcoin chain data into an AG repository named bitcoin using 4 loader processes:

./convert.py \
–source=http://btcuser:btcpassword@localhost:8332 \
–destination=http://aguser:agpassword@localhost:10035 \
–name=bitcoin \
–workers=4 \
–clear

Example queries
Following are the examples of using SPARQL to extract different information about block data:

• number of known blocks:

PREFIX : <https://raw.githubusercontent.com/franzinc/agraph-examples/master/data/bitcoin/model.ttl#>
SELECT (COUNT(*) AS ?count) WHERE { ?b a btcm:Block. }

• total number of transactions:

PREFIX : <https://raw.githubusercontent.com/franzinc/agraph-examples/master/data/bitcoin/model.ttl#>
SELECT (COUNT(*) AS ?count) WHERE { ?tx a btcm:Transaction. }

• transaction in block 400:

PREFIX : <https://raw.githubusercontent.com/franzinc/agraph-examples/master/data/bitcoin/model.ttl#>
SELECT ?txid
WHERE {
?b a :Block.
?b :height “570001”^^xsd:int.
?b :transaction ?tx.
?tx :txid ?txid.
}

• transactions sending more than 1000 BTC:

PREFIX : <https://raw.githubusercontent.com/franzinc/agraph-examples/master/data/bitcoin/model.ttl#>
SELECT ?tx
WHERE {
?b a :Block.
?b :transaction ?tx.
?tx :output ?out.
?out :amount ?amt.
}
GROUP BY ?tx
HAVING (SUM(?amt) > 100000000000)

• transactions sending BTC to Pirate Bay’s address:

PREFIX : <https://raw.githubusercontent.com/franzinc/agraph-examples/master/data/bitcoin/model.ttl#>
SELECT ?tx
WHERE {
?tx :output ?out.
?out :lockScript ?s.
FILTER REGEX (?s, “<tpb address>”).
}

Franz Inc. is proud to announce that it has been named to the 2020 Trend Setting Products in Data Management by Database Trends and Application Magazine.

Database Trends and Applications (DBTA) magazine announced its seventh annual list of trend-setting products in data management and analysis. The list, “DBTA Trend-Setting Products for 2020,” recognizes products in the marketplace that are both innovative and effective in helping customers address evolving challenges and opportunities. In all, 100 products are highlighted in the special December edition of Database Trends and Applications magazine and on the DBTA website, www.dbta.com.

“The world of data management and analytics continues to evolve rapidly with new technologies and strategies,” remarked Thomas Hogan, Group Publisher of Database Trends and Applications. “Cutting through the hype and identifying products that deliver results in the real world is more important than ever. This list highlights products that are truly transformative in bringing greater agility, efficiency and innovation to market.”

“We are honored to receive this acknowledgement for our efforts in delivering Enterprise Knowledge Graph Solutions,” said Dr. Jans Aasman, CEO, Franz Inc. “In the past year, we have seen demand for Enterprise Knowledge Graphs take off across industries along with recognition from top technology analyst firms that Knowledge Graphs provide the critical foundation for artificial intelligence applications and predictive analytics.   Our AllegroGraph Knowledge Graph Platform Solution offers a unique comprehensive approach for helping companies accelerate the creation of Enterprise Knowledge Graphs that deliver new value to their organization.”

KMWorld 2019 Readers’ Choice Awards: Best Knowledge Graph

AllegroGraph – Finalist

The ability for knowledge graphs to amass information and relationships and connect those facts allows companies to find context in data, which is important for extracting value as well as complying with new data regulations.

The concept of the enterprise knowledge graph (EKG) is fairly new and made possible by machine learning and big data technologies, including automated text analysis and graph engines, explained analyst Amy Stapleton in an Opus Research article. “An IA [intelligent assistant] that taps into an EKG can infer the context and intent of questions, generate direct answers, make recommendations, and automatically expand its understanding as the knowledge graph adds new content,” she noted.

KMWorld Readers Choice

Graph-Driven Event Processing for Intelligent Customer Operations

Wednesday, October 16, 2019
10:15 AM – 11:15 AM
Level: Case Study

In the typical 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, 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. The Intelligent Customer Operations center is enabled by a taxonomy of the products and services sold, speech recognition to turn conversations into text, a taxonomy-driven entity extractor to take the important concepts out of conversations, and machine learning to classify chats in various ways. All of this is stored in a real-time Knowledge Graph that also knows (and stores) everything about customers and agents and provides the raw data for machine learning to improve the agent/customer interaction.

In this presentation, we describe a real-world Intelligent Customer Organization 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.

https://graphorum2019.dataversity.net/sessionPop.cfm?confid=132&proposalid=11010

Franz’s CEO, Jans Aasman’s recent Marine Technology News:

Data’s valuation as an enterprise asset is most acutely realized over time. When properly managed, the same dataset supports a plurality of use cases, becomes almost instantly available upon request, and is exchangeable between departments or organizations to systematically increase its yield with each deployment.

These boons of leveraging data as an enterprise asset are the foundation of GO FAIR’s Findable Accessible Interoperable Reusable (FAIR) principles profoundly impacting the data management rigors of geological science. Numerous organizations in this space have embraced these tenets to swiftly share information among a diversity of disciplines to safely guide the stewardship of the earth.

According to Dr. Annie Burgess, Lab Director of Earth Science Information Partners (ESIP), the “most pressing global challenges cannot be solved by a single organization. Scientists require data collected across multiple disciplines, which are often managed by many different agencies and institutions.” As numerous members of the earth science community are realizing, the most effectual means of managing those disparate data according to FAIR principles is by utilizing the semantic standards underpinning knowledge graphs.

Read the full article at Marine Technology News

The Ontology Summit is an annual series of events that involves the ontology community and communities related to each year’s theme chosen for the summit. The Ontology Summit was started by Ontolog and NIST, and the program has been co-organized by Ontolog, NIST, NCOR, NCBO, IAOA, NCO_NITRD along with the co-sponsorship of other organizations that are supportive of the Summit goals and objectives.

Knowledge graphs, closely related to ontologies and semantic networks, have emerged in the last few years to be an important semantic technology and research area. As structured representations of semantic knowledge that are stored in a graph, KGs are lightweight versions of semantic networks that scale to massive datasets such as the entire World Wide Web. Industry has devoted a great deal of effort to the development of knowledge graphs, and they are now critical to the functions of intelligent virtual assistants such as Siri and Alexa. Some of the research communities where KGs are relevant are Ontologies, Big Data, Linked Data, Open Knowledge Network, Artificial Intelligence, Deep Learning, and many others.

Dr. Jans Aasman presented – “Why Knowledge Graphs Hit the Hype Cycle and What they have in common”

Presentation Page

Presentation Slides