Python offers excellent libraries for working with graphs: semantic technologies, graph queries, interactive visualizations, graph algorithms, probabilistic graph inference, as well as embedding and other integrations with deep learning.

However, most of these approaches share little common ground, nor do many of them integrate effectively with popular data science tools (pandas, scikit-learn, spacy, pytorch), nor efficiently with popular data engineering infrastructure such as Spark, RAPIDS, Ray, Parquet, fsspect, etc.

In this podcast episode, Paco Nathan reviews kglab – an open source project that integrates most all of the above, and moreover provides ways to leverage disparate techniques in ways that complement each other, to produce Hybrid AI solutions for industry use cases.

Slides available: https://derwen.ai/s/kcgh

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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-manipulation
• Large-scale knowledge bases—likely leveraging innate frameworks—that incorporate symbolic knowledge along with other forms of knowledge
• Reasoning mechanisms capable of leveraging those knowledge bases in tractable ways
• And 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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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 blocks
• Information model expressivity
• Machine understandable representations
• Transcending the relational model
• How an EKG expands on a graph and a knowledge graph
• Provides an infrastructure for Machine Learning
• Contrasting an unlinked with linked data environment
• Question and answering model emergence
• Semantic similarity & embedding
• Focused UI

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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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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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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 beyond
• The Knowledge Graph Development Lifecycle: Building, Consolidating and Managing Knowledge Graphs

Featuring 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

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 reasoning
• Reasoning in industry
• Getting started with reasoning
• The future of reasoning

Target Audience

• Developers
• Data Engineers
• Technical Leaders
• Management
• CxOs
• Investors

Goals

• 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 & axioms
• The reasoning standards and beyond

Where 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-device
• What 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.

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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.

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

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 Learning
• Deep Learning
• Graph Data Management
• Knowledge Graphs
• Graph ML in Production

Target Audience

• Machine Learning Practitioners
• Data Scientists
• Data Modelers
• CxOs
• Investors

Goals

• Explore the interplay between machine learning and knowledge based technologies
• How 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?
• NLP
• Biomed
• Industry (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:

• Introduction
• Meet and Greet
• Setting the stage
• Knowledge Graphs, meet Graph Machine Learning
• It’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 art
• An 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 tasks
• Where is this used in production?
• Success stories and business cases
• What 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 production
• Who are some key players to keep an eye on?
• Both from industry and research

Panelists:

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

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