The Connected Data Podcast | CDW CDW Team

Most Major Companies are Exploring or Using Knowledge Graphs.


Knowledge Graphs are at the top of the Garter AI Hype Cycle.


But Knowledge Graphs are much more than hype!


Knowledge graphs are a mature technology used in large scale deployments.


Anyone heard of Google, Facebook, Alibaba, or Uber?


Knowledge graphs address major weaknesses in traditional relational technology.


These weaknesses are major drivers for silos, the bane of every enterprise.


Knowledge Graphs are being deployed in a large variety of industries, including financial services, information technology, health care & life sciences, manufacturing and media.


Common use cases include data harmonization, search, recommendation, question answering, entity resolution, provenance, & security.


Join Ashleigh Faith, Katariina Kari, Michael Uschold and Mike Atkin from our Connected Data World 2021 panel discussion as they address the following:


Key Topics


What are Knowledge Graphs good for?Supporting technologiesHow can I get started?What roadblocks should I watch out for?

Target Audience


Chief Data OfficersData ScientistsData ModelersTechnical Managers

Goals


Understand where and how knowledge graphs can add value to your enterpriseKnow what supporting technologies are required for a typical knowledge graph deploymentKnow how to get started on a knowledge graph application in your enterprise.Understand what the current state of the art is and what is on the horizon.Be aware of possible roadblocks to avoid.

#CDW21 kicks off with a live discussion among our top-notch contributors and Program Committee members, and you're all invited!


Join us as we have a sneak peek through the CDW21 program, and discuss the Connected Data landscape.


The CDW21 Program Committee members will go through the 50+ sessions and 70+ speakers, and talk about:


The Connected Data landscapeKnowledge GraphsGraph DatabasesGraph AnalyticsGraph Data Science &Semantic TechnologyTopics, speakers and talks that piqued our interestOur own work in the domain and how it cross-cuts #CDW21Community chat and Ask Me AnythingMore in-depth topics as time permits:Hiring a team for building knowledge graphs: required roles and skills, what can be taught? I want a knowledge graph! What next? The process of starting to build a knowledge graph for an organisation: assessment of need, use cases, support needed from management etc.Triple Store vs Labelled Property Graphs: It's not either-or, it's both and more!

With an all-star Program Committee and lineup, this will be a tour de force in Connected Data.

ABOUT THIS TALK


What does graph have to do with machine learning?


A lot, actually. And it goes both ways


Machine learning can help bootstrap and populate knowledge graphs.


The information contained in graphs can boost the efficiency of machine learning approaches.


Machine learning, and its deep learning subdomain, make a great match for graphs. Machine learning on graphs is still a nascent technology, but one which is full of promise.


Amazon, Alibaba, Apple, Facebook and Twitter are just some of the organizations using this in production, and advancing the state of the art.


More than 25% of the research published in top AI conferences is graph-related.


Domain knowledge can effectively help a deep learning system bootstrap its knowledge, by encoding primitives instead of forcing the model to learn these from scratch.


Machine learning can effectively help the semantic modeling process needed to construct knowledge graphs, and consequently populate them with information.


Key Topics


What can knowledge-based technologies do for Deep Learning?What is Graph AI, how does it work, what can it do?What's next? What are the roadblocks and opportunities?

Target Audience


Machine Learning PractitionersData ScientistsData ModelersCxOsInvestors

Goals


Explore the interplay between machine learning and knowledge based technologiesAnswer questions that matterHow can those approaches complement one another, and what would that unlock?What is the current state of the art, how and where is it used in the wild?What are the next milestones / roadblocks?Where are the opportunities for investment?

Session outline


IntroductionMeet and GreetSetting the stageKnowledge Graphs, meet Machine LearningHow can machine learning help create and populate knowledge graphs?What kind of problems can we solve by using it?Where is this used in production?What is the current state of the art in knowledge graph bootstrapping and population?What are the major roadblocks / goals, how could we address them, and what would that enable?Who are some key players to keep an eye on?Graph Machine LearningWhat is special about Graph Machine Learning?What kind of problems can we solve by using it?Where is it used in production?What is the current state of the art?What are the major roadblocks / goals, how could we address them, and what would that enable?Who are some key players to keep an eye on?

Format


Extended panelExpert discussion, coordinated by moderator2 hours running timeRunning time includes modules of expert discussion, interspersed with modules of audience Q&A / interaction

Level


Intermediate - Advanced

Prerequisite Knowledge


Basic understanding of Knowledge GraphsBasic understanding of Machine Learning / Deep Learning

Abstract:


 


Personalized medicine. Predictive call centers. Digital twins for IoT. Predictive supply chain management, and domain-specific Q&A applications. These are just a few AI-driven applications organizations across a broad range of industries are deploying.


 


Graph databases and Knowledge Graphs are now viewed as a must-have by Enterprises serious about leveraging AI and predictive analytics within their organization.


 


See how Franz Inc. is helping organizations deploy novel Entity-Event Knowledge Graph Solutions to gain a holistic view of customers, patients, students or other important entities, and the ability to discover deep connections, uncover new patterns and attain explainable results.


 


Description:


 


To support ubiquitous AI, a Knowledge Graph system will have to fuse and integrate data, not just in representation, but in context (ontologies, metadata, domain knowledge, terminology systems), and time (temporal relationships between components of data). Building from ‘Entities’ (e.g. Customers, Patients, Bill of Materials) requires a new data model approach that unifies typical enterprise data with knowledge bases such as industry terms and other domain knowledge.


 


Entity-Event Knowledge Graphs are about connecting the many dots, from different contexts and throughout time, to support and recommend industry-specific solutions that can take into account all the subtle differences and nuisances of entities and their relevant interactions to deliver insights and drive growth. The Entity-Event Data Model we present puts core entities of interest at the center and then collects several layers of knowledge related to the entity as ‘Events’.


 


Franz Inc. is working with organizations across a broad range of industries to deploy large-scale, high-performance Entity-Event Knowledge Graphs that serve as the foundation for AI-driven applications for personalized medicine, predictive call centers, digital twins for IoT, predictive supply chain management and domain-specific Q&A applications—just to name a few.


 


During this presentation we will explain and demonstrate how Entity-Event Knowledge Graphs are the future of AI in the Enterprise.

Talk recorded at 2017 Connected Data London Conference





Connected Data encompasses data acquisition and data management requirements from a range of areas including the Semantic Web, Linked Data, Knowledge Management, Knowledge Representation and many others. Yet for the true value of many of these visions to be realised both within the public domain and within organisations requires the assembly of often huge datasets. Thus far this has proven problematic for humans to achieve within acceptable timeframes, budgets and quality levels.


This panel discussion by Paul Groth, Spyros Kotoulas, Tara Rafaat, Freddy Lecue & moderator Szymon Klarman tackles these issues

"The most important contribution management needs to make in the 21st Century is to increase the productivity of knowledge work and the knowledge worker", said Peter F. Drucker in 1999, and time has proven him right.






Even NASA is no exception, as it faces a number of challenges. NASA has hundreds of millions of documents, reports, project data, lessons learned, scientific research, medical analysis, geospatial data, IT logs, and all kinds of other data stored nation-wide.






The data is growing in terms of variety, velocity, volume, value and veracity. NASA needs to provide accessibility to engineering data sources, whose visibility is currently limited. To convert data to knowledge a convergence of Knowledge Management, Information Architecture and Data Science is necessary.






This is what David Meza, Acting Branch Chief - People Analytics, Sr. Data Scientist at NASA, calls "Knowledge Architecture": the people, processes, and technology of designing, implementing, and applying the intellectual infrastructure of organizations.