Webinar: How Metadata Management Must Evolve to Support Data Fabric

Looks to be useful, plan to attend:

TopQuadrant: How Metadata Management Must Evolve to Support Data Fabric   by Irene Polikoff | Mar 8, 2022 | Metadata Management, Webinars

About This Webinar

On Thu, Mar 17, 2022 11:30 AM EDT

If you have not heard the term “data fabric” yet, you will. It is rapidly growing in popularity. Gartner identified data fabric as the top trend for data and analytics in 2021.

You can think of data fabric as a web connecting multiple locations, types, and sources of data – both on-premises and in the public cloud. It is an architectural approach designed to help organizations better deal with the growing number of available data sources and ever-changing application requirements. The backbone of data fabric design is a Knowledge Graph capturing information about data sources in RDF. This is a new type of a data catalog with semantically augmented and enriched metadata.

Join us for this webinar to:

Learn What Is Data Fabric

Understand Why Data Fabric Requires a Knowledge Graph

Get Advice on Moving from Traditional Metadata Management to Metadata Management With Knowledge Graphs

Envision How Tools Participating in Data Fabrics Will Interact With Knowledge Graphs

See these Concepts in Action  ... 

 Register:   https://register.gotowebinar.com/register/7807142108219203083 

Alexa Entities Build with Knowledge Graphs

Announcements in the Alexa Development blog about new means to connect knowledge graph data to voice interactions with Entities.    With code and Dev examples.   Technical.

Announcing Alexa Entities (Beta): Create More Intelligent and Engaging Skills With Easy Access to Alexa’s Knowledge  ....

Launch Productivity Build

We are excited to announce Alexa Entities (Beta), a new suite of tools that provides access to information about popular entities including people, places and things from Alexa’s Knowledge Graph. With Alexa Entities, you can now resolve strings in a customer’s utterance to common entities from a built-in catalog, using those entities as an entry point to traverse Alexa’s structured knowledge. Alexa Entities is currently supported for all customers with 15 Built-in Slot Types in English (US) and English (CA). Check out the technical documentation here to start building skills with Alexa’s knowledge.

Alexa Entities provides access to Alexa’s high-quality, continuously updated Knowledge Graph containing facts about popular entities including people (e.g. George Clooney’s birthday), places (e.g. the population and capital of Belgium) and things (e.g. the average weight of a hippo). 

Why Use Alexa Entities

Build More Intelligent & Engaging Skills: Alexa Entities makes Built-in Slot Types more useful by linking entities to Alexa’s knowledge, which can be used to build more engaging and intelligent experiences for customers. Connections between entities can be used to create more natural dialogues, for example “Add Alias Grace to my reading list”, “Got it. Have you thought about reading The Testaments, also written by Margaret Atwood in 2019?” Knowledge can also be used to easily disambiguate between similarly named entities, e.g. “Did you mean Anne Hathaway, born in 1556, or the actress in films such as Les Misérables?” or simply presented on-screen to create more engaging visual experiences. For example, the company Vocala recently used Alexa Entities to improve their new skill “Voice Blast”, which allows customers to compete against other players to guess celebrity voices. Alexa’s knowledge is used to provide additional facts about celebrities on-screen, complementing the spoken response with useful & interesting information. Simply say “Alexa, play Voice Blast” to see Alexa Entities in action.  ... "

On Knowledge Graphs

Great piece on a favorite topic from CACM.  Only mildly technical.  Its all about usefully and efficiently representing knowledge.  Essential for anyone considering the future of string and using knowledge.  We experimented with it in the enterprise, for both historical and operational purposes. Below quick intro, more at the link.

Key Insights:

Data was traditionally considered a material object, tied to bits, with no semantics per se. Knowledge was traditionally conceived as the immaterial object, living only in people's minds and language. The destinies of data and knowledge became bound together, becoming almost inseparable, by the emergence of digital computing in the mid-20h century.

Knowledge Graphs can be considered the coming of age of the integration of knowledge and data at large scale with heterogeneous formats.

The next generation of researchers should become aware of these developments. Both successful and not, these ideas are the basis of current technology and contain fruitful ideas to inspire future research.

Knowledge Graphs    By Claudio Gutierrez, Juan F. Sequeda

Communications of the ACM, March 2021, Vol. 64 No. 3, Pages 96-104   10.1145/3418294

The notion of Knowledge Graph stems from scientific advancements in diverse research areas such as Semantic Web, databases, knowledge representation and reasoning, NLP, and machine learning, among others. The integration of ideas and techniques from such disparate disciplines presents a challenge to practitioners and researchers to know how current advances develop from, and are rooted in, early techniques.

Understanding the historical context and background of one's research area is of utmost importance in order to understand the possible avenues of the future. Today, this is more important than ever due to the almost infinite sea of information one faces everyday. When it comes to the Knowledge Graph area, we have noticed that students and junior researchers are not completely aware of the source of the ideas, concepts, and techniques they command.

The essential elements involved in the notion of Knowledge Graphs can be traced to ancient history in the core idea of representing knowledge in a diagrammatic form. Examples include: Aristotle and visual forms of reasoning, around 350 BC; Lull and his tree of knowledge; Linnaeus and taxonomies of the natural world; and in the 19th. century, the works on formal and diagrammatic reasoning of scientists like J.J. Sylvester, Charles Peirce and Gottlob Frege. These ideas also involve several disciplines like mathematics, philosophy, linguistics, library sciences, and psychology, among others.  ... " 

Knowledge Graphs vs Property Graphs

Robert Coyne Writes and points to a number of their resources on this topic:

Thank you for attending our webinar: "Knowledge Graphs vs. Property Graphs --a brief overview and comparison". We hope you enjoyed the event. .... .

The recording and slides from the webinar are available here: https://www.topquadrant.com/knowledge-graphs-vs-property-graphs-a-brief-overview-and-comparison/. We have also linked a blog post with the questions and answers from attendees here: https://www.topquadrant.com/graphs-overview-and-comparison/

Also, as mentioned in the webinar, you can find the "Property Graphs vs. Knowledge Graphs" white paper here: https://www.topquadrant.com/knowledge-assets/whitepapers/

Should you have any follow-up questions or would like to explore all of the capabilities in TopBraid EDG (http://www.topquadrant.com/products/topbraid-enterprise-data-governance/).)    ....

Cases: NASA and Others Using Knowledge Graphs

This is particularly interesting because NASA has a very broad use context for analytics, and thus the underlying knowledge that drives them.   Upcoming talk should be of interest, I plan to attend.  Note all of their sessions are stored and easy to retrieve.

Don't miss the NASA session!  (Tomorrow)
Hi Franz,

Let’s talk Knowledge Graphs!   At this month’s Connections, a digital event series, we will be exploring all things graph technology. We’re taking a deep dive into the world of Knowledge Graphs and the potential contextual searches hold for your business.

Knowledge graphs relate structured and unstructured data (often disparate and spread across your organization) to help identify information and reveal important but hidden facts. They are also necessary for creating semantic AI applications that inherently thrive on contextual connections.

Join us on Tuesday, August 25 from 07:30-11:30 PT / 14:30-18:30 UTC for a full day of sessions with speakers from NASA, BMO Financial Group, and more!

Can’t make it for the full day? Don’t worry! All talks will be shared with registered attendees after the event. Save your spot now!        http://neo4j.com

REGISTER FOR CONNECTIONS
https://message.neo4j.com/CRn00x0G10GR0NYv2w0EC01

Knowledge Graphs vs Property Graphs

Upcoming Webinar of interest, register at the link.

Knowledge Graphs vs. Property Graphs — A Brief Overview and Comparison
by Kristi Lee-John | Jul 29, 2020 | Webinars, Knowledge Graphs,  TopQuadrant

About This Webinar   Aug 13 @ 11:30AM ET

We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.

Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say “the semantic twenties,” we also see vendors that never before mentioned graphs starting to position their products and solutions as graphs or graph-based.
Graph databases are one thing, but “Knowledge Graphs” are an even hotter topic. We are often asked to explain Knowledge Graphs.

Today, there are two main graph data models:
Property Graphs (also known as Labeled Property Graphs)
RDF Graphs (Resource Description Framework) aka Knowledge Graphs
Other graph data models are possible as well, but over 90% of the implementations use one of these two models. In this webinar we will cover the following:

I. A brief overview of each of the two main graph models noted above
II. Differences in Terminology and Capabilities of these models
III. Strengths and Limitations of each approach
IV. Why Knowledge Graphs provide a strong foundation for enterprise data governance and metadata management .... '

More about the Webinar, registration.

Challenge and Workshop for Open Domain Question Answering

Answer questions based on open domain Knowledge.  Good general statement of the most important part of useful AI.   Details at the link.

Presenting a Challenge and Workshop in Efficient Open-Domain Question Answering 
\
Posted by Eunsol Choi, Visiting Faculty Researcher and Tom Kwiatkowski, Research Scientist, in Google Research Blog 

One of the primary goals of natural language processing is to build systems that can answer a user's questions. To do this, computers need to be able to understand questions, represent world knowledge, and reason their way to answers. Traditionally, answers have been retrieved from a collection of documents or a knowledge graph. For example, to answer the question, “When was the declaration of independence officially signed?” a system might first find the most relevant article from Wikipedia, and then locate a sentence containing the answer, “August 2, 1776”. However, more recent approaches, like T5, have also shown that neural models, trained on large amounts of web-text, can also answer questions directly, without retrieving documents or facts from a knowledge graph. This has led to significant debate about how knowledge should be stored for use by our question answering systems — in human readable text and structured formats, or in the learned parameters of a neural network.

Today, we are proud to announce the EfficientQA competition and workshop at NeurIPS 2020, organized in cooperation with Princeton University and the University of Washington. The goal is to develop an end-to-end question answering system that contains all of the knowledge required to answer open-domain questions. There are no constraints on how the knowledge is stored — it could be in documents, databases, the parameters of a neural network, or any other form — but entries will be evaluated based on the number of bytes used to access this knowledge, including code, corpora, and model parameters. There will also be an unconstrained track, in which the goal is to achieve the best possible question answering performance regardless of system size. To build small, yet robust systems, participants will have to explore new methods of knowledge representation and reasoning. ... " 

Harnessing Graphs in a New Business Climate

Of interest, upcoming:

Tuesday, July 14    8:00 a.m. PT | 11:00 a.m. ET

Harnessing Graphs in a New Business Climate

Hi Franz,
As economies continue to reopen across much of Europe and North America, many organizations turn to graph technology to help rapidly reconfigure and reset operations.

Graphs are perfectly suited for handling connected data, like tracing connections through complex networks. Graphs can also identify complex relationships faster than human-only efforts by combining multiple isolated datasets and identifying missing data points.

Join us for this panel discussion with key Neo4j partners who are using graph technology to help organizations balance efficiency and innovation in today’s challenging times.

Presenters

Dr. Alessandro Negro, Chief Scientist, GraphAware
Demian Bellumio, Global Vice President of Augmented Intelligence, NEORIS
Weidong Yang, CEO, Kineviz
Axel Morgner, Founder and Managing Director, Structr
Martin Preusse, Founder at Kaiser & Preusse
Lance Walter, CMO, Neo4j

REGISTER TODAY

Hope to see you there,     Lance Walter    Neo4j

Golden: An Intelligent Knowledge Base

Brought to my attention:  Golden, quite a considerable breadth of claims.

The intelligent knowledge base
Explore the world's first self-constructing knowledge database built by artificial and human intelligence.

Authoritative knowledge at your fingertips
Access a growing body of knowledge. Follow topics you're interested in. Explore new topics to create a personalized knowledge feed.

Query across the Golden Knowledge Base
The Golden Research Engine is a comprehensive knowledge tool to research and track information on specific topics including companies, investment funds, cryptocurrencies, crypto projects, people and more.

Request deep information on a topic
Click a button to trigger fast turnaround of full information surrounding a topic of interest. Trigger research requests on information around a query and our AI-enabled helpers will max out the data request.

Frictionless tools to compile knowledge
Golden is building an interface to compile and comprehend knowledge. Frictionless editing, enhanced fact validation systems, transparent version histories and topic tracking.

A new standard for evidence
Golden will compile deeper evidence to validate claims and verify knowledge. High-resolution citations and bibliometrics will allow users to determine the provenance of information and evaluate source credibility.

Human knowledge meets AI
Golden draws on the strengths of both humans and machines. Statistical models and heuristic algorithms will handle repetitive work and automate the process of gathering knowledge.  ... " 

Microsoft Project Cortex

Brought to my attention from this weeks Build meetings as about to be launched.   Form of knowledge management.  Had been show very early version of this.  Notion of a semantic Web has been around for a long time, though not used often enough.   Will be following this.

Project Cortex

Today, we’re pleased to introduce Project Cortex, the first new service in Microsoft 365 since the launch of Microsoft Teams. Project Cortex uses advanced AI to deliver insights and expertise in the apps you use every day, to harness collective knowledge and to empower people and teams to learn, upskill and innovate faster.

Project Cortex uses AI to reason over content across teams and systems, recognizing content types, extracting important information, and automatically organizing content into shared topics like projects, products, processes and customers. Cortex then creates a knowledge network based on relationships among topics, content, and people.

New topic pages and knowledge centers—created and updated by AI—enable experts to curate and share knowledge with wiki-like simplicity. And topic cards deliver knowledge just-in-time to people in Outlook, Microsoft Teams, and Office.  ... " 

Google Knowledge Graphs, Knowledge Panels

Want to build from these basics. an introduction:

A reintroduction to our Knowledge Graph and knowledge panels
Danny Sullivan In Google Blog
Public Liaison for Search

Sometimes Google Search will show special boxes with information about people, places and things. We call these knowledge panels. They’re designed to help you quickly understand more about a particular subject by surfacing key facts and to make it easier to explore a topic in more depth. Information within knowledge panels comes from our Knowledge Graph, which is like a giant virtual encyclopedia of facts. In this post, we’ll share more about how knowledge panels are automatically generated, how data for the Knowledge Graph is gathered and how we monitor and react to reports of incorrect information.

What’s a knowledge panel?
Knowledge panels are easily recognized by those who do desktop searching, appearing to the right of search results:    ... " 

Paper on the Future of the Intelligent, Graphic Application

In the past have liked Neo4j papers on this and related topics of graph analytics and intelligence.  Reading and may review here further.   9 pages, generally non-technical and descriptive.

White Paper
Neo4j White Paper: The Future of the Intelligent Application
The Future of the Intelligent Application
By Patrick Wall

Summary:
Legacy data technologies aren’t up to the task of powering modern, intelligent applications.

Tomorrow’s solutions require full data context to support smart decision- making in real time. They must incorporate intelligence and learning at every step, breaking down the barrier between operational systems and the data science that powers new insights.

This white paper prepares software architects and developers for the challenges of intelligent application development – and illustrates why graph database technology is key to meeting these emerging requirements, including:

Read the White Paper:

Unlimited scalability
Robust security and data privacy
Agile, reactive architecture
Fill out the form to get your copy of The Future of the Intelligent Application: Why Graph Databases Will Power Tomorrow’s Connected Solutions.   ... "

Knowledge Graphs versus Property Graphs

This came in the mail, it had been asked in an interaction recently.  Worth a look.  We worked with TopQuadrant.

New White Paper: Knowledge Graphs versus Property Graphs

We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.

The two main graph data models are: Property Graphs and Knowledge (RDF) Graphs. People who want to take advantage of graph-based solutions for data and metadata management want to know what they are, what are their similarities and differences, and what they are each good for.

This white paper covers the following, it:
Describes the two main graph data models: Property Graphs and RDF Graphs and explains the key differences in their terminology and capabilities
Compares their strengths and limitations
Provides guidance on their respective capabilities

Other TopQuadrant resources to explore:
RECORDED WEBINARS, including this most recent one: "Getting Started with Data Governance"
WHITE PAPER COLLECTION, including: "Implementing Data Governance with Knowledge Graphs to Enable Enterprise AI"

Download Now    https://www.topquadrant.com/knowledge-assets/whitepapers/

This email sent by TopQuadrant   www.topquadrant.com

On Metadata and Cooking

In almost every piece of work I have done in enterprises, there has been a need to deal with metadata.  Metadata can mean a number of things .... like for example 'data about data'.  for example the number of hits in a search defining data.    But in most of the cases I have worked with 'metadata'  means supporting data, and is often data that comes from a different source, that may be hard to find, or may need to be specially created for an effort.

Well done piece in the BiPolar blog by Matthew Roche.  Which compares metadata to recipe based cooking.

BI Polar
Business Intelligence, Data Governance, Mental Health, Diversity, Martial Arts, and Heavy Metal.
Metadata is Not a “Nice to have” 

He also has other posts on metadata I have not read yet.

Amazon Develops AI to Improve Knowledge Graph Performance

More about knowledge graphs,  their quick manipulation can be key to provide intelligence to the edge for assistance.

Amazon researchers develop AI that improves knowledge graph performance
  By Kyle Wiggers in VentureBeat 

In a new study  researchers at Amazon describe a technique that factors in information about knowledge graphs to perform entity alignment, which entails determining which elements of different graphs refer to the same “entities” (which might be anything from products to song titles). The idea is to improve computational efficiency while at the same time improving performance, speeding up graph-related tasks like product searches on Amazon and question answering via Alexa.

The work, which was accepted to the 2020 Web Conference, might also benefit graphs beyond Amazon, such as those that underpin social networks like Facebook and Twitter, as well as graphs used by enterprises to organize various digital catalogs.  .... "

Microsoft Project Cortex: Optimizing the Enterprise ?

Was reminded of this, announced generally last year.    It actually has similarities to some things we discussed with Linkedin a number of years ago: To use a company's internal organization chart, enhanced by a 'knowledge graph', to provide more intelligent and efficient internal and external communications further driven by AI.   A more precisely semantic way to organize how cloud/data is linked to task/process?  To ultimately optimize how a company works?  Following.

Introducing Project Cortex

Project Cortex

Today, we’re pleased to introduce Project Cortex, the first new service in Microsoft 365 since the launch of Microsoft Teams. Project Cortex uses advanced AI to deliver insights and expertise in the apps you use every day, to harness collective knowledge and to empower people and teams to learn, upskill and innovate faster.

Project Cortex uses AI to reason over content across teams and systems, recognizing content types, extracting important information, and automatically organizing content into shared topics like projects, products, processes and customers. Cortex then creates a knowledge network based on relationships among topics, content, and people.

New topic pages and knowledge centers—created and updated by AI—enable experts to curate and share knowledge with wiki-like simplicity. And topic cards deliver knowledge just-in-time to people in Outlook, Microsoft Teams, and Office.  .... "

Knowledge Graphs in Industry at Scale

How knowledge Graphs are done, used in industry.  Emphasizing realistic scale.

Industry-Scale Knowledge Graphs: Lessons and Challenges
By Natasha Noy, Yuqing Gao, Anshu Jain, Anant Narayanan, Alan Patterson, Jamie Taylor
Communications of the ACM, August 2019, Vol. 62 No. 8, Pages 36-43  10.1145/3331166

Knowledge graphs are critical to many enterprises today: They provide the structured data and factual knowledge that drive many products and make them more intelligent and "magical."

In general, a knowledge graph describes objects of interest and connections between them. For example, a knowledge graph may have nodes for a movie, the actors in this movie, the director, and so on. Each node may have properties such as an actor's name and age. There may be nodes for multiple movies involving a particular actor. The user can then traverse the knowledge graph to collect information on all the movies in which the actor appeared or, if applicable, directed.

Many practical implementations impose constraints on the links in knowledge graphs by defining a schema or ontology. For example, a link from a movie to its director must connect an object of type Movie to an object of type Person. In some cases the links themselves might have their own properties: a link connecting an actor and a movie might have the name of the specific role the actor played. Similarly, a link connecting a politician with a specific role in government might have the time period during which the politician held that role.

Knowledge graphs and similar structures usually provide a shared substrate of knowledge within an organization, allowing different products and applications to use similar vocabulary and to reuse definitions and descriptions that others create. Furthermore, they usually provide a compact formal representation that developers can use to infer new facts and build up the knowledge—for example, using the graph connecting movies and actors to find out which actors frequently appear in movies together.

This article looks at the knowledge graphs of five diverse tech companies, comparing the similarities and differences in their respective experiences of building and using the graphs, and discussing the challenges that all knowledge-driven enterprises face today. The collection of knowledge graphs discussed here covers the breadth of applications, from search, to product descriptions, to social networks:

Both Microsoft's Bing knowledge graph and the Google Knowledge Graph support search and answering questions in search and during conversations. Starting with the descriptions and connections of people, places, things, and organizations, these graphs include general knowledge about the world.

Facebook has the world's largest social graph, which also includes information about music, movies, celebrities, and places that Facebook users care about.

The Product Knowledge Graph at eBay, currently under development, will encode semantic knowledge about products, entities, and the relationships between them and the external world.

The Knowledge Graph Framework for IBM's Watson Discovery offerings addresses two requirements: one focusing on the use case of discovering nonobvious information, the other on offering a "Build your own knowledge graph" framework. .... "

The Helix Organization and Beyond

The matrix doesn't work well anymore.   So I think there should also be stronger integration to knowledge and decision making.  And linkages to machines and data that represent knowledge.  There will always be outages in what people know, or can do.   Can a knowledge graph of data and agents fill in the gaps?  Detect the gaps?  Organize the solution?

The helix organization
In McKinsey
By Aaron De Smet, Sarah Kleinman, and Kirsten Weerda

Separating people-leadership tasks from day-to-day business leadership can help organizations strike a better balance between centralization and decentralization, reduce complexity, and embrace agility.

The CEO of a major global business, deeply frustrated, took time out recently as a large company-wide reorganization was stumbling toward its conclusion. Hard as he and his top team had tried, he told us, attempts to make collaboration and empowerment an enterprise-wide reality were foundering. Although he had been determined to ensure resources were reallocated across the group more dynamically, people and money remained doggedly stuck in slightly revamped silos. Tensions between the group’s central functions—such as finance, HR, and IT—and the group’s decentralized businesses were continuing to rumble. As he gazed at a new organization chart on his laptop, he scratched his head while trying to make sense of the complex collection of solid- and dotted-line reporting relationships floating across the screen.

As our business environment has become more complex and interconnected, we seem to be replicating that in our organizations, creating complex matrix structures that simply don’t work anymore.

The CEO in question is actually a composite of several with whom we’ve had different versions of this same conversation. Their frustrations, in turn, are similar in spirit to concerns we hear almost daily from many other senior executives. As our business environment has become more complex and interconnected, we seem to be replicating that in our organizations, creating complex matrix structures that simply don’t work anymore. We are overreliant on the same management tools for organization structure that we’ve been using for decades, namely hierarchical org charts with solid- and dotted-line reporting relationships.

There are no easy answers to deep-rooted organizational dysfunction. However, we’re increasingly convinced that there is a simple, exciting, and effective structural model that can replace complex matrix structures and help leaders across industries and geographies who struggle with confused roles and labored decision-making processes, and who feel they are failing to move quickly enough to exploit new market opportunities.

The “helix,” as we’ve dubbed it, is not a new idea. It has been around for decades in professional-service firms and in parts of some large global companies, and more recently in many agile enterprises. But until now, it has lacked a name and clear definition, and its power to unlock organizational bottlenecks and to strike a better balance between centralization and decentralization has never been properly articulated. It is seldom implemented at significant scale, and many organizations that initially embrace it slide back to more traditional (and often less effective) structures. That’s no coincidence. For reasons we will discuss, successfully adopting the helix requires management mind-sets and a talent infrastructure that many businesses do not currently possess.    .... "

Amazon Enlists Companies to Enhance Voice

Of interest, it came to mind early on in the enterprise that such AI assistants needed to work together, with the same log-in and access to the same semantic databases, Knowledge Graphs.   Sharing skills and specialized capabilities.   That does not seem to be what this is about, but it is perhaps a first step in the same direction.  Will be following closely.

Amazon enlists 30 companies to improve how voice assistants work together  That includes multiple assistants on the same device that support multiple wake words.   Christine Fisher, @cfisher writes in Engadget

Just because you have an Amazon device doesn't mean you should be limited to interacting with Alexa -- or so Amazon believes. Today, the company announced a new Voice Interoperability Initiative. The goal is to work with other companies so that users can access multiple voice services -- from Alexa to Cortana and Salesforce's Einstein -- on a single device.

"The initiative is built around a shared belief that voice services should work seamlessly alongside one another on a single device, and that voice-enabled products should be designed to support multiple simultaneous wake words," Amazon wrote in a press release.

More than 30 companies have signed on, including brands like BMW, Bose, ecobee, Microsoft, Salesforce, Sonos, Spotify and Samsung-owned Harman. Missing from the lineup of partners are companies like Google, Apple and Samsung -- all three of which have their own voice assistants and dedicated devices. ... " 

Building Knowledge Graphs

Have been looking at means of continuously and coherently connecting company data sources to analytical and AI methods.    Most recently have looked at the idea of 'Knowledge Graphs'.   Of interest, an upcoming webinar by Neo4j on knowledge graphs, here particularly on financial services, but applicable beyond that.   Note in particular the mention of 'intelligent metadata',  which we posed to construct understandable and maintainable data sources.   Will be attending.

Financial Services Companies Make Disparate Data Simple with Knowledge Graphs Webinar

Tuesday, September 24    11:00 am PST

Knowledge graphs are driving industry disruption and business transformation by bringing together previously disparate data, using connections for superior decision support, and adding context for more intelligent applications (including AI). In this session, we’ll walk through the fundamental elements of knowledge graphs including contextual relevance, dynamic self-updating, understandability with intelligent metadata, and the combination of heterogeneous data. ....'

More information and register here.