Afleveringen
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Industry leaders from Accenture, Johnson & Johnson, and the Enterprise Knowledge Graph Foundation dive deep into the transformative potential of knowledge graphs, exploring how these semantic technologies are revolutionizing enterprise data management.
Featuring Mike Atkin, Laurent Alquier and Teresa Tung.
The conversation reveals a critical shift from traditional data processing to a more nuanced, context-rich approach that prioritizes data meaning and reusability. Participants discuss how organizations are moving beyond experimental pilots to enterprise-wide implementations, driven by a growing recognition that data incongruence is a significant liability in today's data-driven business landscape.
The discussion unveils the key challenges of knowledge graph adoption:
* Overcoming organizational inertia
* Bridging technological gaps, and
* Fundamentally changing mindsets about data representation.
Experts share insights into the importance of telling compelling stories about knowledge graphs, focusing on business value rather than technical complexity. They emphasize the need for incremental implementation, collaborative approaches, and the crucial role of knowledge engineers who can translate between technical capabilities and business needs.
We've arrived at a pivotal moment for enterprise knowledge graphs: the technology has matured, business leaders are increasingly receptive, and there's a growing understanding that these semantic technologies offer more than just another IT solution.
Knowledge graphs represent a fundamental reimagining of how organizations can capture, understand, and leverage their data—moving away from the myth of a single version of truth towards a more flexible, context-rich approach that allows multiple perspectives to coexist. For businesses looking to remain competitive in a data-driven world, the message is clear: the time to start building knowledge graphs is now.
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Michael Atkin has over 30 years of experience as a strategic analyst to financial institutions, regulators and market authorities on the principles, practices and operational realities of data management.
Dr Laurent Alquier's current role is to shape the architecture, design and development of J&J’s Knowledge Sharing ecosystem to further enable Emerging Technologies and Innovation management, Enterprise Architecture, and other IT strategic capabilities.
Teresa Tung is a Managing Director at Accenture Labs responsible for taking the best-of-breed next-generation architecture solutions from industry, start-ups, and academia, and for evaluating their impact on Accenture's clients through building experimental prototypes and delivering pioneering pilot engagements.
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Gary Marcus argues for a shift in research priorities, towards four cognitive prerequisites for building robust artificial intelligence:
Hybrid architectures that combine large-scale learning with the representational and computational powers of symbol-manipulationLarge-scale knowledge bases—likely leveraging innate frameworks—that incorporate symbolic knowledge along with other forms of knowledgeReasoning mechanisms capable of leveraging those knowledge bases in tractable waysAnd rich cognitive models that work together with those mechanisms and knowledge bases.Although there are real problems to be solved here, and a great deal of effort must go into constraining symbolic search well enough to work in real time for complex problems, Google Knowledge Graph seems to be at least a partial counterexample to this objection, as do large scale recent successes in software and hardware verification.
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Gary Marcus is a scientist, best-selling author, and entrepreneur. He is Founder and CEO of Robust.AI, and was Founder and CEO of Geometric Intelligence, a machine learning company acquired by Uber in 2016.
He is the author of five books, including The Algebraic Mind, Kluge, The Birth of the Mind, and The New York Times best seller Guitar Zero, as well as editor of The Future of the Brain and The Norton Psychology Reader.
Gary has published extensively in fields ranging from human and animal behavior to neuroscience, genetics, linguistics, evolutionary psychology and artificial intelligence, often in leading journals such as Science and Nature, and is perhaps the youngest Professor Emeritus at NYU. His newest book, co-authored with Ernest Davis, Rebooting AI: Building Machines We Can Trust aims to shake up the field of artificial intelligence.
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For more insightful content be sure to visit Connected Data London 2024 and purchase tickets Connected Data London 2024
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Zijn er afleveringen die ontbreken?
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Join Omar Khan and David Newman as they canvas the Enterprise Knowledge Graph, and how you can apply it using its cornerstones of:
Foundational building blocksInformation model expressivityMachine understandable representationsTranscending the relational modelHow an EKG expands on a graph and a knowledge graphProvides an infrastructure for Machine LearningContrasting an unlinked with linked data environmentQuestion and answering model emergenceSemantic similarity & embeddingFocused UI---
David Newman provides leadership and expertise for the advancement of knowledge graph solutions at Wells Fargo. His team develops innovations that employ key knowledge graph capabilities, including ontology models, semantic and property graph databases, graph analytics, knowledge graph embeddings and graph visualization techniques.
David’s core mission is to actualize the potential of knowledge graph at Wells Fargo by creating a collaborative knowledge graph modeling community, developing enterprise standards and best practices, and creating operational pipelines for the ingestion, transformation and consumption of data using knowledge graphs.
David’s initiatives include leveraging knowledge graph technology to fulfill business use cases by creating expressive enterprise and line of business ontologies, knowledge driven data asset catalogs, linked operational knowledge graphs and applying machine learning algorithms that train on knowledge graphs.
David also chairs the Financial Industry Business Ontology (FIBO) initiative, a collaborative effort of global banks, financial regulators and vendors, under the auspices of the Enterprise Data Management Council (EDMC). Their goal is to semantically define a common language standard for finance using ontologies.
Omar Khan is presently a member of Data Management & Insights, fostering Wells Fargo efforts and building applications as Technical Lead in Knowledge Graph & Semantic Technologies. Prior to his current role, Omar built novel solutions for the business during an 11-year tenure as a consultant and full-time employee within Brokerage Technology.
While with Brokerage Technology, Omar helped to develop many key applications, and led efforts contributing to a majority of the IT portfolio in Wealth and Investment Management.
A few years ago he became known for contributing to proof of concepts in areas unexplored, but necessary for future changes in direction for various lines of businesses.
Omar successfully implemented game-changing software development ideas, and this helped form a foundation to allow me to join Innovation Group's R&D, and subsequently Data Management & Insights, specializing in Enterprise Knowledge Graph technologies. Emerging technology was and still is his specialty and passion.
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👉 For more on Knowledge Graphs, Graph Data Science and AI, Graph Databases and Semantic Technology, join Connected Data London this December - Book Your Ticket Now
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Graph representation learning has recently become one of the hottest topics in machine learning.
One particular instance, graph neural networks, is being used in a broad spectrum of applications ranging from 3D computer vision and graphics to high energy physics and drug design.
Despite the promise and a series of success stories of graph deep learning methods, we have not witnessed so far anything close to the smashing success convolutional networks have had in computer vision.
In this Michael Bronstein outlines his views on the possible reasons and how the field could progress in the next few years.
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Michael Bronstein is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition, and Head of Graph Learning Research at Twitter. He also heads ML research in Project CETI, a TED Audacious Prize-winning collaboration aimed at understanding the communication of sperm whales.
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👉 For more Deep Learning on Knowledge Graphs, Graph Data Science and AI, Graph Databases and Semantic Technology, join Connected Data London this December - Book Your Ticket Now
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Connected Data is coming back to London in 2024, on December 11-13.
Join us for a tour de force in all things Knowledge Graph, Graph Analytics / Al / Data Science / Databases and Semantic Technology.
Call for submissions and volunteers, program committee, chairs, and initial lineup have been announced.
This online roundtable highlights the Connected Data landscape and how it's reflected in our Call for Submissions, while it also goes over the event's format and answers audience questions.
Key topics:
How taxonomy, ontology and knowledge graphs can help GenAI and Large Language Models: Graph RAG and beyondThe Knowledge Graph Development Lifecycle: Building, Consolidating and Managing Knowledge GraphsFeaturing Connected Data Founders George Anadiotis and James Phare, Program chairs Amy Hodler and Paco Nathan, and Program Committee Members Panos Alexopoulos, Giuseppe Futia, Heather Hedden, Juan Sequeda, Ivo Velitchkov and Andrea Volpini.
Call for submissions: https://www.connected-data.london/call-for-submissions
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What does reasoning have to offer? How does it add so much value to data? Who is using it and why should I care?
All questions that we’re delighted to answer.
Access to data has exploded over the last decade, but it leaves us asking what to make of it all? Often lacking quality, reasoning is required to enrich data by adding context and insights, serving up knowledge, not just numbers.
This expert panel will explore the who, what, why, and how of reasoning: Its foundations, its advancements over the years, and its bright future.
Google, Amazon and Facebook are just a few of the giants implementing reasoning today to great effect. With that said, this is not a tool exclusive to the Fortune 500—intelligence is buried in data everywhere, a valuable asset at any scale.
Key Topics
A technical introduction to reasoningReasoning in industryGetting started with reasoningThe future of reasoningTarget Audience
DevelopersData EngineersTechnical LeadersManagementCxOsInvestorsGoals
To introduce the concepts of reasoning & the theoretical foundations that support it.To explore the role of reasoning in industry—who, what, why, and how.To examine the opinions of sector leaders as to the future of reasoning.What is the current state of the art, how and where is it used in the wild?Session outline:
Meet the panel
An introduction to reasoning
What separates a knowledge graph from a simple graph?What is semantic reasoning?Logical consequences, facts & axiomsThe reasoning standards and beyondWhere is reasoning used in production?
What kind of problems are being solved with reasoning?Who uses this technology?What is the current state of reasoning in industry?From data modelling to the technical stack
What are your options for reasoning today?Where can reasoning be deployed? From Cloud to Edge to on-deviceWhat performance can you expect from a system with reasoning?Where do I start?
What do I need before I start reasoning?Who has the skills I need?My company is resistant, why should they change?Reasoning a future
What does the future of reasoning look like?What are the challenges?How will we get there?Format
A series of short presentations by reasoning experts, each followed by a discussion from the panel, coordinated by moderator.Audience interaction and questions are encouraged.2 hours running time.---
Panel By Haonan Qiu , Ian Horrocks , Ora Lassila , Marcus Nölke , Peter Crocker And Tara Raafat
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Connected Data London 2024 has been announced!.
December 11-13, etc Venues St. Paul’s, City of London
Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london
To keep up with updates in Knowledge Graphs, Graph Data Science and AI, Graph Databases and Semantic Technology subscribe for Connected Data London (CDL) blogs, newletters.
Meet over 1,000 industry professionals by registering to attend Connected Data London (CDL) held from 11-13 of December in London. The 2024 edition will be our finest and biggest event to date, featuring our tried and true recipe of bringing together leaders and innovators in Masterclasses, Keynotes, Presentations, Workshops and Panels, plus lots of new and exciting features such as Networking and Unconference sessions, a Gala Dinner and Speaker Lounge.
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An AI tsunami is on the rise, and the past few months have only amplified it. To survive it and thrive in tomorrow’s economy, organizations big and small must rethink the way they do business. To do this, a radical shift in the way they work with their data is needed. And no, we don’t mean Big Data.
By now, most organizations have gotten their Big Data. And that is a problem. Not because we can’t accommodate Big Data, but because the more data you have, the harder it becomes to connect it and use it. We need to go beyond Big Data, towards Connected Data.
We’ll show how enterprises can use decentralized Knowledge Graphs to vastly increase the connectivity of their data, drawing on hard won experience of architecting and successfully delivering innovative technical projects for the world’s largest financial organisations.
Large enterprises that want to survive the AI tsunami must undergo a profound transformation in the way they think about their data. It starts by accepting that they need to link a large percentage of ALL their data together into a unified whole.
Achieving this will require a radical rethink of some established ideas about enterprise data integration. The truth is meaningful data doesn't exist in isolation; everything is positioned within the context of everything else.
That is why the future of data is graph shaped … but what are graphs and what is so great about them?
Knowledge Graphs are a really powerful tool, but on their own, they are not enough to transform enterprise data integration. We also need to get our heads around the complex idea of decentralisation. In a decentralised data mesh, the responsibility for data integration is pushed down to the individual applications.
Unbeknownst to most people, a third of all web pages now contain little islands of data that help the search engines build their knowledge graphs. Enterprises do not need to reinvent the wheel to build themselves a Decentralised Enterprise Knowledge Graph. They can just take this battle-hardened web tech and use it behind their firewall to connect their internal data.
In other words, the tools for this job already exist but enterprises are not yet using them internally. In this talk, we’ll share the hard won experience of how this was done for the world’s largest financial organizations.
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Tony Seale is a Knowledge Graph Architect at UBS. An experienced software architect and polyglot programmer with a proven track record of successfully delivering Knowledge Graphs into production for Tier 1 investment banks.
He has been exclusively focused on building decentralized Knowledge Graphs for the last ten years and has given talks, produced videos and written articles to promote the technology.
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Connected Data London 2024 has been announced!.
December 11-13, etc Venues St. Paul’s, City of London
Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london
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Graph-based technologies became first-class citizens in various industries and many practical applications. Still, building performant and reliable machine learning pipelines over graph data, e.g., graph machine learning applications and products, remains a non-trivial task.
This panel discussion brings together academic and industrial experts from fields where Graph ML yields significant gains and greatly improves traditional processes. In addition to highlighting successful business cases, the panel concentrates on questions often dismissed or hidden behind the curtains of modern Graph ML applications.
In particular, we will talk about the origins of graph data, its modeling, organization, and processing aspects; best communication interfaces; bridging a gap between products and ML algorithms as well as measuring their practical impact.
On a higher level, the panel will discuss upcoming trends in industrial Graph ML and prospective disruptive applications.
Key Topics
Graph Machine LearningDeep LearningGraph Data ManagementKnowledge GraphsGraph ML in ProductionTarget Audience
Machine Learning PractitionersData ScientistsData ModelersCxOsInvestorsGoals
Explore the interplay between machine learning and knowledge based technologiesHow to get the “actionable” knowledge from the graph data?How 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?NLPBiomedIndustry (say, Ad Tech)What are the next milestones / roadblocks?Where are the opportunities for investment?We know drug discovery is on its highs, NLP is being democratized rapidly, what else?Session outline:
IntroductionMeet and GreetSetting the stageKnowledge Graphs, meet Graph Machine LearningIt’s all about the data:How do you create, maintain, and process graphs?Databases or tabular sources?Do you consider data modeling aspects?Best communication interface: natural language or structured query languages?How can machine learning help create and populate graphs (including KGs)?Cover some of the current state of the artAn edge - does it appear naturally or derived from node similarities?What kind of problems can we solve by using it?NLP, Biomed, Industry (say, adtech)How academic datasets align with real-world tasksWhere is this used in production?Success stories and business casesWhat are the major roadblocks / goals, how could we address them, and what would that enable?How to bridge the gap between business goals and Graph ML models?How do we measure the impact of applying ML models in real-world tasks? Metrics, A/B testing, generally about setting things in productionWho are some key players to keep an eye on?Both from industry and researchPanelists:
Mikhail Galkin. Researcher, Mila | McGill University
Dr. Tiffany Callahan. Researcher, University of Colorado, Anschutz Medical Campus
Andreea Deac. Researcher, Mila | Université de Montréal
Dr. Charles Hoyt. Researcher, Harvard Medical School, Laboratory of Systems Pharmacology
Sergei Ivanov. Research Scientist, Criteo AI Lab
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Connected Data London 2024 has been announced!.
December 11-13, etc Venues St. Paul’s, City of London
Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london
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After the amazing breakthroughs of machine learning (deep learning or otherwise) in the past decade, the shortcomings of machine learning are also becoming increasingly clear: unexplainable results, data hunger and limited generalisability are all becoming bottlenecks.
In this talk we will look at how the combination with symbolic AI (in the form of very large knowledge graphs) can give us a way forward, towards machine learning systems that can explain their results, that need less data, and that generalise better outside their training set.
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Frank van Harmelen leads the Knowledge Representation & Reasoning group in the CS Department of the VU University Amsterdam. He is also Principal investigator of the Hybrid Intelligence Centre, a 20Μ€, 10 year collaboration between researchers at 6 Dutch universities into AI that collaborates with people instead of replacing them.
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Slides available at: https://www.slideshare.net/slideshow/systems-that-learn-and-reason-frank-van-harmelen/267008886
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Connected Data London 2024 has been announced!.
December 11-13, etc Venues St. Paul’s, City of London
Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london
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What do graphs have to do with novel hardware architectures for AI workloads?
Graph processing is the key to unlocking new architectures, as much as new architectures can boost execution of graph-oriented workloads.
As machine learning-powered applications are proliferating, the workloads that are created in order to serve their requirements are taking up an ever increasing piece of the compute pie.
An IDC study found that Data Management, Application Development & Testing, and Data Analytics workloads represented more than half of all IaaS and PaaS spending already in 2018. IDC notes that this was driven in part by initial adoption of artificial intelligence and machine learning capabilities.
The rise of generative AI means that as adoption grows, data and AI workloads will dominate. This is why we see NVIDIA earnings skyrocket, as well as a renaissance of novel hardware architectures designed from the ground up to serve the needs of data and AI workloads.
More specifically for data analytics, understanding relationships among data points is a challenging but essential capability. Graph analytics has emerged as an approach by which analysts can efficiently examine the structure of the large networks and draw conclusions from the observed patterns. This is why DARPA set out to develop a graph analytics processor with the HIVE Project.
Furthermore, all machine learning models are best expressed as graphs. This is how machine learning libraries such as TensorFlow work. Efficient processing of graph-based networks involves large sparse data structures that consist of mostly zero values, and next generation architectures should avoid unnecessary processing.
This panel explores the interrelationship between graph processing and novel AI hardware architectures. Hosted by ZDNet's Tiernan Ray with panelists from some of the most groundbreaking AI hardware companies: Blaize, Determined AI / HPE, Graphcore, and SambaNova.
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Tiernan Ray. Contributing Writer, ZDNet
Tiernan Ray has been covering technology & business for 27 years. He was most recently technology editor for Barron's where he wrote daily market coverage for the Tech Trader blog and wrote the weekly print column of that name. He has also worked for Bloomberg, SmartMoney, and for the prestigious ComputerLetter newsletter covering venture capital investments in tech
Val G. Cook. Chief Software Architect, Blaize
Val G. Cook is Chief Software Architect at Blaize. An AI visionary and authority on the design of graphics and visual computing architectures, Val possesses two decades of experience in graphics and multimedia algorithms and software architecture. He is responsible for the Blaize Graph Streaming Processor software programming environment.
Carlo Luschi. Director of Research, Graphcore
Carlo is responsible for the study and development of algorithms for machine intelligence. Prior to Graphcore, Carlo was a Member of Technical Staff at Bell Labs Research, Lucent Technologies, and more recently Director of Algorithms and Standards at Icera Inc., which was acquired by NVIDIA in 2011.
Raghu Prabhakar. Software Engineer, SambaNova
Raghu Prabhakar is a senior principal engineer and one of the founding engineers at AI innovation platform SambaNova Systems. His research interests are in the areas of programming models, compilers, and hardware architecture for reconfigurable dataflow architectures.
Evan Sparks. Founder, Determined AI, an HPE Company
Evan Sparks, Vice President of Artificial Intelligence and High Performance Computing at HPE, co-founded Determined AI (now an HPE company). His group helps businesses get better AI-powered solutions to market faster and delivers the open source Determined Training Platform for large scale AI model development.
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While mathematicians have used graph theory since the 18th century to solve problems, the software patterns for graph data are new to most developers. To enable "mass adoption" of graph technology, we need to establish the right abstractions, access APIs, and data models.
RDF triples, while of paramount importance in establishing RDF graph semantics, are a low-level abstraction, much like using assembly language. For practical and productive “graph programming” we need something different.
Similarly, existing declarative graph query languages (such as SPARQL and Cypher) are not always the best way to access graph data, and sometimes you need a simpler interface (e.g., GraphQL), or even a different approach altogether (e.g., imperative traversals such as with Gremlin).
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Ora Lassila is a Principal Graph Technologist in the Amazon Neptune graph database group. He has a long experience with graphs, graph databases, ontologies, and knowledge representation. He was a co-author of the original RDF specification as well as a co-author of the seminal article on the Semantic Web.
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Presentation slides available at https://www.slideshare.net/slideshows/graph-abstractions-matter-by-ora-lassila/266140641
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Connected Data London 2024 has been announced!.
December 11-13, etc Venues St. Paul’s, City of London
Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london
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Taxonomies are the duct tape of connected data. They seem simple, flexible, and familiar. They are widely used. And they seem to work across many use cases and many domains.
But when looked at in more detail, taxonomies turn out to be crude tools for knowledge organization that are very difficult to create, to scale, to adapt, to align, and to build on.
They don't work well for larger or more complex domains and use cases. Experienced talent and flexible tools for creating them are hard to find and to develop. Often taxonomies are built then abandoned for other, more robust approaches to knowledge organization.
It is essential to re-evaluate your connected data strategies in the context of alternative approaches to knowledge organization.
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Mike Dillinger. Technical Lead for Taxonomies and Ontologies, AI Division, LinkedIn
Mike Dillinger, PhD, focuses on teaching machine learning algorithms about the world of work at LinkedIn. Before that, he was Technical Lead for LinkedIn’s and eBay’s first machine translation systems, and an independent consultant specialized in deploying translation technologies for Fortune 500 companies.
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Connected Data London 2024 has been announced!.
December 11-13, etc Venues St. Paul’s, City of London
Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london
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What is Connected Data, and how is it interesting from a market point of view?
Knowledge Graphs have reached peak Gartner hype. Graph data science and graph AI are the fastest growing areas in AI. Graph databases are the fastest growing category in enterprise software.
Add to this the historical foundations of graph algorithms and analytics and semantic technology, which have been invigorated and are seeing widespread adoption, and you get the burgeoning Connected Data landscape.
While there is ongoing technical innovation happening in the domain, how does this translate to market value and opportunities for investment?
How is this market defined, and what is driving its growth?
Join us as we define and explore this landscape, discuss technology and use cases, challenges and opportunities for growth and investment, and where the future may take us.
Join George Anadiotis, Panos Papadopoulos, Bob van Luijt and Konstantin Vinogradov from our Connected Data World 2021 panel discussion as they address the following:
Key Topics
Defining the Connected Data technology and market landscapeExploring the Connected Data marketProviding an outlook for the futureTarget Audience
EntrepreneursTechnical people with entrepreneurial spiritCxOsDecision makersInvestorsGoals
Define and explore the Connected Data landscape for people who are interested in it from a market perspectiveAnswer questions that matterHow is this market defined?What are some key drivers for growth?Where are the opportunities for investment?What is the outlook for the future?---
Connected Data London 2024 has been announced!.
December 11-13, etc Venues St. Paul’s, City of London
Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london
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JSON is the de facto data format for developers today because it’s easy to use, but it’s not without its issues. JSON-LD builds on top of JSON, and has also been called "the gateway drug" for Linked Data.
Our panel of experts explores the many facets of JSON-LD and how it can facilitate enterprise data integration. Featuring Kurt Cagle, Freelance Technology Analyst, Brian Platz, co-founder and CEO of Fluree, Benjamin Young, Principal Architect at John Wiley and Sons and co-chair of the W3C JSON-LD Working Group. Moderated by George Anadiotis, Connected Data World Managing Director.
Article published on the Connected Data World blog.
Sponsored by Fluree. Fluree’s platform enables trusted, linked, and composable data, combining the ease of JSON documents with the power of linked data.
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Connected Data London 2024 has been announced!.
December 11-13, etc Venues St. Paul’s, City of London
Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london
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Graph Analytics has long demonstrated that it solves real-world problems including Fraud, Ranking, Recommendation, text summarization and other NLP tasks.
More recently, Graph Machine Learning applied directly on graphs using graph algorithms and machine learning, has been demonstrating significant advantages in solving the same problems as graph analytics as well as problems that are impractical to solve using graph analytics. Graph Machine Learning does this by training statistical models on the graph resulting in Graph Embeddings and Graph Neural Networks that are used to complex problems in a different way.
Jörg Schad, ArangoDB CTO, compares and contrasts these two approaches (spoiler: often complexity vs precision) in real-world scenarios. What factors should you consider when choosing one over the other and when do you even have a choice? Learn about exciting new developments in Graph ML and the graph techniques on which they are based.
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Connected Data London 2024 has been announced!.
December 11-13, etc Venues St. Paul’s, City of London
Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london
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Are your personal data, documents, files and messages all over the place?
Do you find yourself switching between applications, devices and files, unable to remember or find what you were looking for?
Would it make you feel better to know that it's not entirely your fault, and maybe there is a way out?
You know the stories about how the volume of data the world generates every day has gone through the roof. You know how most platforms want to lock you and your data in.
The volume and complexity of data each person has to manage today is comparable to what business owners and knowledge management professionals had to manage a few years ago.
What if each one of us could use the tools and practices professionals use to manage their data and build knowledge, while avoiding vendor lock-in?
A new generation of tools aiming to democratize access to knowledge management best practices and technology previously reserved for professional use is on the rise.
These tools, geared towards personal use, come in many shapes and forms. But they have one thing in common: they treat connections and context as first-class citizens, leveraging the graph paradigm.
Join us as we explore the rise of the Personal Knowledge Graph, and discuss use cases, tools, features and functionality, challenges and opportunities, and how to get started.
This panel will help define and explore the interplay between the most advanced technology for managing data and knowledge and user-oriented tools.
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Connected Data London 2024 has been announced!.
December 11-13, etc Venues St. Paul’s, City of London
Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london
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The AI industry is now facing its next big challenge.
What are the necessary properties of representational structures that could allow vast amounts of data become meaningful in the human sense of the word?
How can knowledge architectures be constructed in a way that allows for both the efficiency and effectiveness of models they support?
In his Connected Data World 2021 keynote, Gadi Singer, VP & Director of Emergent AI at Intel Labs, discusses anthropomorphic conceptual structures and their benefits for enhancing Cognitive AI capabilities.
A visionary concept and a keynote which is even more timely today than it was then, foreseeing many current and, dare we say, future developments.
Singer introduces his model of the three levels of knowledge – Thrill-K – which can serve as a blueprint for building AI systems that are both efficient and scalable.
He begins with an Introduction to the Next Wave of AI. He addresses Language Models such as GPT-3, their shortcomings as Knowledge Models, and how they can be used in combination with Knowledge Graphs.
He then lists Five Essential Capabilities of Great Knowledge Models and describes the Thrill-K Architecture. Singer concludes by referring to The Future of AI, Cognitive AI and Deep Knowledge.
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Connected Data London 2024 has been announced!.
December 11-13, etc Venues St. Paul’s, City of London
Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london
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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 ManagersGoals
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.---
Connected Data London 2024 has been announced!.
December 11-13, etc Venues St. Paul’s, City of London
Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london
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Join us as we have a sneak peek through the Connected Data World 2021 program, and discuss the Connected Data landscape.
Our Program Committee members 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.
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Connected Data London 2024 has been announced!.
December 11-13, etc Venues St. Paul’s, City of London
Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london
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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 ModelersCxOsInvestorsGoals
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 / interactionLevel
Intermediate - AdvancedPrerequisite Knowledge
Basic understanding of Knowledge GraphsBasic understanding of Machine Learning / Deep Learning---
Connected Data London 2024 has been announced!.
December 11-13, etc Venues St. Paul’s, City of London
Check #CDL24 for more Presentations, Keynotes, Masterclasses, and Workshops on cutting-edge topics from industry leaders and innovators: https://connected-data.london
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