Graph-Based Data Science: Hybrid AI Meets Data Science Process

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Squid Game: The Official Podcast

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לפני 20 ימיםלפני 20d ago

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The final season is here—and Squid Game: The Official Podcast is your ultimate companion to the end of the Game. Hosts Phil Yu and Kiera Please return once more to break down every shocking twist and betrayal, and the choices that will determine who, if anyone, makes it out alive. Will Player 456 and the cast of characters we’ve grown to love finally be able to dismantle the games for good? Or will the cycle continue? Alongside creators, cultural critics, and viral internet voices, Phil and Kiera provide their own theories for how the season ends, and what ⁠Squid Game⁠ ultimately reveals about power, sacrifice, and the systems that shape us. The biggest question isn’t who wins—it’s what it means to be human. Squid Game: The Official Podcast returns Friday, June 27th.

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Graph-Based Data Science: Hybrid AI meets data science process | Paco Nathan

לפני שנה 36:23

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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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If you liked this podcast, check #CDL24 for more Presentations, Keynotes, Masterclasses, and Panels on cutting-edge topics from industry leaders and innovators:

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The Connected Data Podcast

Graph-Based Data Science: Hybrid AI meets data science process | Paco Nathan

The Connected Data Podcast

published לפני שנה

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

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If you liked this podcast, check #CDL24 for more Presentations, Keynotes, Masterclasses, and Panels on cutting-edge topics from industry leaders and innovators:

https://2024.connected-data.london/

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#Tech #CDL Team

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The Connected Data Podcast

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Graph-Based Data Science: Hybrid AI meets data science process | Paco Nathan 36:23

לפני 29 weeks36:23

36:23

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 --- If you liked this podcast, check #CDL24 for more Presentations, Keynotes, Masterclasses, and Panels on cutting-edge topics from industry leaders and innovators: https://2024.connected-data.london/…

The Connected Data Podcast

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Enterprise Knowledge Graphs: Breaking Through Organizational Inertia to Reimagine Data Management | Panel Discussion 39:03

לפני 34 weeks39:03

39:03

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. -- 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. -- For more insightful content be sure to visit Connected Data London 2024 and purchase tickets Connected Data London 2024…

The Connected Data Podcast

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Rebooting AI: Adding Knowledge to Deep Learning | Gary Marcus 38:53

לפני 38 weeks38:53

38:53

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. -- 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. -- For more insightful content be sure to visit Connected Data London 2024 and purchase tickets Connected Data London 2024…

The Connected Data Podcast

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The Enterprise Knowledge Graph | Omar Khan and David Newman 32:34

לפני 42 weeks32:34

32:34

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 --- 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. -- 👉 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…

The Connected Data Podcast

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Deep Learning on Graphs: Past, Present, And Future | Michael Bronstein 29:33

לפני 47 weeks29:33

29:33

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. -- 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. -- 👉 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…

The Connected Data Podcast

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Connected Data London 2024 Call for Submissions Roundtable | Panel Discussion 1:26:36

לפני 51 weeks1:26:36

1:26:36

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…

The Connected Data Podcast

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The Momentum Behind Semantic Reasoning and Why It’s Here to Stay | Panel Discussion 1:31:21

לפני 1 year1:31:21

1:31:21

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. --- Panel By Haonan Qiu , Ian Horrocks , Ora Lassila , Marcus Nölke , Peter Crocker And Tara Raafat --- 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.…

The Connected Data Podcast

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Data Revolution: The Emergence of the Decentralized Enterprise Knowledge Graph | Tony Seale 31:40

לפני 1 year31:40

31:40

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. --- 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. --- 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…

The Connected Data Podcast

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Graph Machine Learning - Research and Industry Applications | Panel Discussion 1:35:38

לפני 1 year1:35:38

1:35:38

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 --- 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…

The Connected Data Podcast

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Systems that learn and reason | Frank Van Harmelen 28:16

לפני 1 year28:16

28:16

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. -- 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. -- Slides available at: https://www.slideshare.net/slideshow/systems-that-learn-and-reason-frank-van-harmelen/267008886 --- 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…

The Connected Data Podcast

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Novel AI Hardware Architectures for Graph Processing | Panel Discussion 1:35:24

לפני 1 year1:35:24

1:35:24

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. --- 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.…

The Connected Data Podcast

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Graph Abstractions Matter | Ora Lassila 22:15

לפני 1 year22:15

22:15

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). -- 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. -- Presentation slides available at https://www.slideshare.net/slideshows/graph-abstractions-matter-by-ora-lassila/266140641 --- 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…

The Connected Data Podcast

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Taxonomies: Connecting Data with Duct Tape | Mike Dillinger 31:28

לפני 2 years31:28

31:28

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. ------ 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. --- 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…

The Connected Data Podcast

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Investing in Connected Data | Panel Discussion 1:48:06

לפני 2 years1:48:06

1:48:06

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 landscape Exploring the Connected Data market Providing an outlook for the future Target Audience Entrepreneurs Technical people with entrepreneurial spirit CxOs Decision makers Investors Goals Define and explore the Connected Data landscape for people who are interested in it from a market perspective Answer questions that matter How 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…

The Connected Data Podcast

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JSON-LD as the pidgin of enterprise data integration | Panel Discussion 1:06:12

לפני 2 years1:06:12

1:06:12

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 . --- 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 vs Graph Machine Learning | Jörg Schad 29:07

לפני 2 years29:07

29:07

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. --- 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…

The Connected Data Podcast

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Personal Knowledge Graphs: A new paradigm for data sovereignty, productivity and creativity | Panel Discussion 2:03:16

לפני 2 years2:03:16

2:03:16

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. --- 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…

The Connected Data Podcast

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Thrill-K: Rethinking knowledge layering and construction for higher machine cognition | Gadi Singer 24:58

לפני 2 years24:58

24:58

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. --- 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…

The Connected Data Podcast

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Knowledge Graphs in the Enterprise: What You Need to Know | Panel Discussion 1:52:32

לפני 2 years1:52:32

1:52:32

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 technologies How can I get started? What roadblocks should I watch out for? Target Audience Chief Data Officers Data Scientists Data Modelers Technical Managers Goals Understand where and how knowledge graphs can add value to your enterprise Know what supporting technologies are required for a typical knowledge graph deployment Know 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…

The Connected Data Podcast

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Connected Data World 2021 Program Roundtable | Panel Discussion 1:33:05

לפני 4 years1:33:05

1:33:05

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 landscape Knowledge Graphs Graph Databases Graph Analytics Graph Data Science & Semantic Technology Topics, speakers and talks that piqued our interest Our own work in the domain and how it cross-cuts #CDW21 Community chat and Ask Me Anything More 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. --- 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…

The Connected Data Podcast

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AI + Knowledge - a match made in heaven? | Panel Discussion 1:51:38

לפני 4 years1:51:38

1:51:38

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 Practitioners Data Scientists Data Modelers CxOs Investors Goals Explore the interplay between machine learning and knowledge based technologies Answer questions that matter 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? What are the next milestones / roadblocks? Where are the opportunities for investment? Session outline Introduction Meet and Greet Setting the stage Knowledge Graphs, meet Machine Learning How 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 Learning What 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 panel Expert discussion, coordinated by moderator 2 hours running time Running time includes modules of expert discussion, interspersed with modules of audience Q&A / interaction Level Intermediate - Advanced Prerequisite Knowledge Basic understanding of Knowledge Graphs Basic 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…

The Connected Data Podcast

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The future of AI in the Enterprise: Entity-Event Knowledge Graphs for Data-Centric Organizations | Jans Aasman 37:22

לפני 4 years37:22

37:22

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. 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. 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. Here's why Entity-Event Knowledge Graphs are the future of AI in the Enterprise. --- 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…

The Connected Data Podcast

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Does Connected Data need AI or AI need Connected Data | Panel Discussion 38:19

לפני 4 years38:19

38:19

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. --- 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…

The Connected Data Podcast

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Κnowledge Architecture: Combining Strategy, Data Science and Information Architecture to Transform Data to Knowledge at NASA | David Meza 49:09

לפני 4 years49:09

49:09

"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. Slides available here https://www.slideshare.net/ConnectedDataLondon/nowledge-architecture-combining-strategy-data-science-and-information-architecture-to-transform-data-to-knowledge-at-nasa --- 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…

The Connected Data Podcast

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Building knowledge graphs in the real world | Panel Discussion 34:29

לפני 4 years34:29

34:29

As the interest in, and hype around, Knowledge Graphs is growing, there is also a growing need for sharing experience and best practices around them. Let’s talk about definitions, best practices, hype, and reality. What is a Knowledge Graph? How can I use a Knowledge Graph & how do i start building one? This panel is an opportunity to hear from industry experts using these technologies & approaches to discuss best practices, common pitfalls and where this space is headed next. --- Katariina Kari Research Engineer, Zalando Tech-Hub Katariina Kari (née Nyberg) is a research engineer at the Zalando Tech-Hub in Helsinki. Katariina holds a Master in Science and Master in Music and is specialised in semantic web and guiding the art business to the digital age. At Zalando she is modelling the Fashion Knowledge Graph, a common vocabulary for fashion with which Zalando improves is customer experience. Katariina also consults art institutions to embrace the digital age in their business and see its opportunities. Panos Alexopoulos Head of Ontology, Textkernel BV Panos Alexopoulos has been working at the intersection of data, semantics, language and software for years, and is leading a team at Textkernel developing a large cross-lingual Knowledge Graph for HR and Recruitment. Alexopoulos holds a PhD in Knowledge Engineering and Management from National Technical University of Athens, and has published 60 papers at international conferences, journals and books. Sebastian Hellman dbpedia.org Sebastian is a senior member of the “Agile Knowledge Engineering and Semantic Web” AKSW research center, focusing on semantic technology research – often in combination with other areas such as machine learning, databases, and natural language processing. Sebastian is head of the “Knowledge Integration and Language Technologies (KILT)” Competence Center at InfAI. He also is the executive director and board member of the non-profit DBpedia Association. Sebastian is also a contributor to various open-source projects and communities such as DBpedia, NLP2RDF, DL-Learner and OWLG, and has been involved in numerous EU research projects. Natasa Varitimou Information Architect, Thomson Reuters Natasa has been working as a Linked Data architect in banking, life science, consumer goods, oil & gas and EU projects. She believes data will eventually become the strongest asset in any organization, and works with Semantic Web technologies, which she finds great in describing the meaning of data, integrating data and making it interoperable and of high quality. Natasa combines, links, expands and builds upon vocabularies from various sources to create flexible and lightweight information easily adaptable to different use cases. She queries these models with their data directly with SPARQL, guarantees data quality based on business rules, creates new information and defines services to bring together diverse data from different applications easily and with the semantics of data directly accessible. --- 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…

The Connected Data Podcast

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Graph for Good: Empowering your NGO | Mario Bastande 32:15

לפני 4 years32:15

32:15

May graph technology improve the deployment of humanitarian projects? The goal of using what we call “ Graphs for good at Action Against Hunger ” is to be more efficient and transparent, and this can have a crucial impact on people’s lives. Is there common behaviour factors between different projects? Can elements of different resources or projects be related? For example, security incidents in a city could influence the way other projects run in there. The explained use case data comes from a project called Kit For Autonomous Cash Transfer in Humanitarian Emergencies ( KACHE ) whose goal is to deploy electronic cash transfers in emergency situations when no suitable infrastructure is available. It also offers the opportunity to track transactions in order to better recognize crisis-affected population behaviours, understanding goods distribution network to improve recommendations, identifying the role of culture in transactional patterns, as well as most required items for every place. Slides available here https://www.slideshare.net/ConnectedDataLondon/graph-for-good-empowering-your-ngo --- 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…

The Connected Data Podcast

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Connecting People, Data, and Apps Without Centralization | Ruben Verborgh 34:11

לפני 4 years34:11

34:11

Big Data has transformed the world big time. It led many companies to strongly focus on data analytics, trying to collect and control gigantic amounts of data. After years in the rat race, several of them are slowly realizing that the continuous striving for having more data than others is maybe not the most meaningful business objective for everyone. In fact, the data collection craze is steadily killing innovation. In this talk, I will discuss post-Big Data thinking in which data is controlled again by people, outlining the goals and ambitions of the Solid project. Check Ruben’s presentation here: https://rubenverborgh.github.io/Connected-Data-London-2019/ --- 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…

The Connected Data Podcast

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Dow Jones: Reimagining the News as a Knowledge Graph | Clancy Childs & Mike Grove 25:18

לפני 4 years25:18

25:18

AI is transforming the financial media industry, impacting everything from content creation to consumption trends. Childs shares insights into how Dow Jones is reimagining what the news looks like. Learn how Dow Jones’ knowledge graph platform – powered by Stardog – enables the company to unify structured and unstructured data from a vast range of news sources and deliver cutting-edge insights for customers and partners globally. Featuring Clancy Childs, former general manager of Dow Jones’ knowledge enablement unit, and Mike Grove, Stardog co-founder --- --- 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…

The Connected Data Podcast

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Knowledge Graphs and SEO: The next chapter | Panel Discussion 1:55:44

לפני 4 years1:55:44

1:55:44

“Knowledge Graph” is an overloaded term. Today Knowledge Graphs are becoming mainstream, and as this happens, more and more people associate Knowledge Graphs with data models, semantics, knowledge management, and ontologies For many other people, however, Knowledge Graphs still mean Google Search Info Boxes, panels, SERPs, and SEO (Search Engine Optimization). They are all right. The term Knowledge Graph was introduced by Google to signify the huge improvement that semantic technology brought to its search engine. Over time, the extended search capabilities and components enabled by semantic technology have become namesakes for Knowledge Graph. While the term Knowledge Graph has more meanings than this, it’s useful to return to the source. The evolution of Knowledge Graph-powered Google search now extends to voice, assimilates information from JSON-LD markup beyond Wikipedia, and advances the state of the art in NLP (Natural Language Processing). Let’s explore how this influences, and is influenced by, advances in semantic technology, where the evolution of SEO is headed, and what this means for knowledge graphs at large. ---- In Knowledge Connexions 2020 , we had the honor and the privilege of hosting Hamlet Batista, alongside Dawn Anderson, David Amerland, Jason Barnard, and Andrea Volpini This great group of people shared their insights on " Knowledge Graphs and SEO: The next chapter " It is with deep sadness that we have learned that Hamlet Batista passed away in January 2021 Though our encounter was brief, we can only attest to the opinions of everyone who knew him: Hamlet was deeply knowledgeable and a pleasure to work with. We share the insights of this panel on the interplay between semantic technology, SEO, and knowledge graphs with the community, as a tribute to Hamlet Batista's memory A talk by Dawn Anderson (Bertey), David Amerland (Davidamerland.com), Jason Barnard (Kalicube), Hamlet Batista (RankSense) & Andrea Volpini (Wordlift). --- --- 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…

The Connected Data Podcast

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Data Model vs Ontology Development – a FIBO perspective | Mike Bennett, 22:04

לפני 5 years22:04

22:04

The Financial Industry Business Ontology, FIBO, is a business conceptual model of how all financial instruments, business entities and processes work in the financial industry. FIBO combines existing financial industry data standards with ontological approaches. It has been developed by the EDM Council, a non-profit global association created to advance Data Management best practices, standards and education. At this podcast, Mike Bennet shares a FIBO perspective on Data Model vs Ontology Development Slides here: https://www.slideshare.net/ConnectedDataLondon/mike-bennett --- 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…

The Connected Data Podcast

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One Ontology, One Data Set, Multiple Shapes with SHACL | Tara Raafat 30:36

לפני 5 years30:36

30:36

Data integration, data interoperation and data quality are major challenges that continue to haunt enterprises. Every enterprise either by choice or by chance has created massive silos of data in different formats, with duplications and quality issues. Knowledge graphs have proven to be a viable solution to address the integration and interoperation problem . Semantic technologies in particular provide an intelligent way of creating an abstract layer for the enterprise data model and mapping of siloed data to that model, allowing a smooth integration and a common view of the data. Technologies like OWL (Web Ontology Language) and RDF (Resource Description Framework) are the back bone of semantics for knowledge graph implementation . Enterprises use OWL to build an ontology model to create a common definition for concepts and how they are connected to each other in their specific domain. They then use RDF to create a triple format representation of their data by mapping it to the Ontology. This approach makes their data smart and machine understandable. But how can enterprises control and validate the quality of this mapped data? Furthermore, how can they use this one abstract representation of data to meet all their different business requirements? Different departments, different LoBs and different business branches all have their own data needs, creating a new challenge to be tackled by the enterprise. In this talk we will look at how the power of SHACL (SHAPES and Constraints Language), a W3C standard for defining constraint sets over data; complements the two core semantic technologies OWL and RDF. What are the similarities, the overlaps and the differences. We will talk about how SHACL gives enterprises the power to reuse, customize and validate their data for various scenarios, uses cases and business requirements; making the application of semantics even more practical. Slides available here https://www.slideshare.net/slideshow/one-ontology-one-data-set-multiple-shapes-with-shacl/180276532 --- 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…

The Connected Data Podcast

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Knowledge graphs, meet Deep Learning | Andy Jefferson 33:13

לפני 5 years33:13

33:13

Knowledge graphs generation is outpacing the ability to intelligently use the information that they contain . Octavian's work is pioneering Graph Artificial Intelligence to provide the brains to make knowledge graphs useful. Our neural networks can take questions and knowledge graphs and return answers. Imagine: a google assistant that reads your own knowledge graph (and actually works) a BI tool reads your business' knowledge graph a legal assistant that reads the graph of your case Taking a neural network approach is important because neural networks deal better with the noise in data and variety in schema. Using neural networks allows people to ask questions of the knowledge graph in their own words , not via code or query languages. Octavian's approach is to develop neural networks that can learn to manipulate graph knowledge into answers. This approach is radically different to using networks to generate graph embeddings. We believe this approach could transform how we interact with databases. Prior knowledge of Neural Networks is not required and the talk will include a simple demonstration of how a Neural Network can use graph data. About the speaker: Andy Jefferson believes that graphs have the potential to provide both a representation of the world and a technical interface that allows us to develop better AI and to turn it rapidly into useful products. Andy combines expertise in machine learning with experience building and operating distributed software systems and an understanding of the scientific process. Before he worked as a software engineer, Andy was a chemist, and he enjoys using the tensor algebra that he learned in quantum chemistry when working on neural networks. Slides available here https://www.slideshare.net/ConnectedDataLondon/knowledge-graphs-meet-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…

The Connected Data Podcast

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Graphs in Sustainable Finance | James Phare 40:47

לפני 5 years40:47

40:47

In recent years graphs have been increasingly adopted in financial services for everything from fraud detection to Know Your Customer (KYC) to regulatory requirements. At the same time Environmental Social Governance (ESG) investing has become the fastest growing segment of financial services. In this session James Phare, Neural Alpha CEO and Founder, discusses how many of these historical graph techniques are now being enhanced for the era of sustainable investing. Going beyond definitions, let's identify use cases, discuss news and trends, and wrap up with an ask me anything session. Graph Databases have been rapidly adopted within financial services since 2008 financial crisis as regulatory drivers have mandated Banks, Asset Managers and others to ramp up efforts to understand their customers better (KYC), report to regulators more precisely in areas such as data lineage and prevent fraud and sanctions breaches. In recent years there has also been a trend towards a ‘greening’ of financial services driven partly by regulators but also by a new generation of financial consumers demanding investments that no longer damage the environment or society at large. Environmental Social Governance (ESG) investing is now the fastest growing segment of the Fund Management industry with the number of funds growing 80% since 2012 to now exceed $1.8tn in assets. In this talk James discusses what he’s been up to recently in his day job as CEO of Neural Alpha – a sustainable fintech based in London. He will give an overview of why he sees graphs as an essential part of the technologists’ toolkit for making the financial industry more sustainable, some of the challenges in large scale, graph centric data integration projects and some of the unique analyses possible from leveraging the power of the graph. James will give a deep dive into how the team are using graph technology to develop tools for the financial industry to more thoroughly screen investments and develop new products. He will also show these tools are being used by NGOs and researchers in the fight against deforestation in projects such as www.trase.finance and in the process are exposing instances of fraud, political corruption, labor rights abuses and other barriers to sustainable investment. Slides available here: https://www.slideshare.net/ConnectedDataLondon/graphs-in-sustainable-finance --- 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…

The Connected Data Podcast

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The Years of the Graph: The Future of the Future is here | George Anadiotis 29:20

לפני 5 years29:20

29:20

What is graph all about, and why should you care? Graphs come in many shapes and forms, and can be used for different applications: Graph Analytics, Graph AI, Knowledge Graphs, and Graph Databases. Up until the beginning of the 2010s, the world was mostly running on spreadsheets and relational databases. To a large extent, it still does. But the NoSQL wave of databases has largely succeeded in instilling the “best tool for the job” mindset. After relational, key-value, document, and columnar, the latest link in this evolutionary proliferation of data structures is graph. Graph analytics, Graph AI, Knowledge Graphs and Graph Databases have been making waves, included in hype cycles for the last couple of years. The Year of the Graph marked the beginning of it all before the Gartners of the world got in the game. The Year of the Graph is a term coined to convey the fact that the time has come for this technology to flourish. The eponymous article that set the tone was published in January 2018 on ZDNet by domain expert George Anadiotis. George has been working with, and keeping an eye on, all things Graph since the early 2000s. He was one of the first to note the continuing rise of Graph Databases, and to bring this technology in front of a mainstream audience. The Year of the Graph has been going strong since 2018. In August 2018, Gartner started including Graph in its hype cycles. Ever since, Graph has been riding the upward slope of the Hype Cycle. The need for knowledge on these technologies is constantly growing. To respond to that need, the Year of the Graph newsletter was released in April 2018. In addition, a constant flow of graph-related news and resources is being shared on social media. To help people make educated choices, the Year of the Graph Database Report was released. The report has been hailed as the most comprehensive of its kind in the market, consistently helping people choose the most appropriate solution for their use case since 2018. The report, articles, news stream, and the newsletter have been reaching thousands of people, helping them understand and navigate this landscape. We’ll talk about the Year of the Graph, the different shapes, forms, and applications for graphs, the latest news and trends, and wrap up with an ask me anything session. Slides available here: https://www.slideshare.net/ConnectedDataLondon/the-years-of-the-graph-the-future-of-the-future-is-here --- Subscribe to our YouTube channel for more gems from the vault: https://www.youtube.com/@ConnectedDataWorld…

The Connected Data Podcast

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From Semantics and SEO to Knowledge Graphs, and Back Again | Panel Discussion 28:25

לפני 5 years28:25

28:25

Knowledge graphs are all the rage these days , but for many they are still an exotic notion which is hard to come to terms with. In this panel, experts who have been working with knowledge graphs before it was cool will share their experience. More specifically, we’ll be looking into the interplay between semantics, SEO, schema.org, JSON-LD, and knowledge graphs. Though it may not be obvious, if you are doing SEO today, you are working with knowledge graphs. Ever since Google popularized the notion of knowledge graphs, it’s been things, not strings. The “things” that search engines can understand are all in schema.org, which is, you guessed it, a framework for building knowledge graphs. Semantic SEO experts Jono Alderson and Andrea Volpini, and expert knowledge graph builder Panos Alexopoulos will share how to onboard yourself to knowledge graphs via schema.org and JSON-LD, as well as the specifics of working with these technologies, and how they can be used to kick-start your own knowledge graphs. We’ll also look at the other direction in this equation: how you can use your knowledge graphs to boost your SEO. Last but not least, we will examine the evolution of schema.org Moderated by David Amerland . Jono Alderson , Yoast. Panos Alexopoulos , Textkernel. Andrea Volpini , Wordlift --- Subscribe to our YouTube channel for more gems from the vault: https://www.youtube.com/@ConnectedDataWorld…

The Connected Data Podcast

1
A 2020 Semantic Web vision for the real world | Panel Discussion 58:13

לפני 5 years58:13

58:13

David Amerland, George Anadiotis, Panos Alexopoulos, and Teodora Petkova, have a few things in common. Besides being successful professionals, each in their own way, they also share a passion not many people share: the Semantic Web. As the Semantic Web is turning 20, they come together to talk about the passion. The first rule of the Semantic Web in the 2020's is, you don't talk about the Semantic Web. Most people don't. For most people, the Semantic Web is something they may be vaguely familiar with, and perhaps something that has been tried, and failed. Truth is, you may not know it, but you use the Semantic Web every day, and you love it. The Semantic Web, soon to celebrate its 20th anniversary, may not enjoy the kind of universal acclaim the WWW got on its 30th birthday, although both were kickstarted by Tim Berners Lee. It does, however, underpin Knowledge Graphs, and in that sense, it is in its heyday. We have known, loved, and used the Semantic Web for a long time, and we'll share why we think you should, too. --- 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…

The Connected Data Podcast

1
RDF and OWL : The powerful duo | Tara Raafat 19:13

לפני 5 years19:13

19:13

Find the answers to: Why go semantic? Should i use RDF or OWL? What is the difference, what is the link? Did you say smart data? In this podcast you can check RDF Integration examples, learn about Ontologies and OWL Presentation by Tara Raafat, (PhD) Chief Ontologist at Mphasis. Slides available at https://www.slideshare.net/ConnectedDataLondon/tara-raafat --- 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…

The Connected Data Podcast

1
Connecting data, decentralizing the web, making it sustainable: can the semantic web do this? | Panel Discussion 41:46

לפני 5 years41:46

41:46

Whether we call it Semantic Web or Linked Data, Tim Berner Lee’s vision never really caught on among users and developers. Although part of this vision is about decentralization, and this is something a few people are working on, Semantic Web technology remains largely underutilized by them. In this panel, we will explore how the Semantic Web and decentralization can benefit each other. Getting together people from both communities, and exploring questions such as: Is the Semantic Web technological stack really as complex as it is perceived to be? How can it be made more accessible, and align better with today’s realities in software development? What are the issues facing people working in decentralization, and how could Semantic Web technology provide solutions? What about sustainability? How can efforts aiming to provide services to the public at large find a way to sustain themselves, navigating a challenging business landscape? Andre Garzia from Mozilla, Sebastian Hellman from DBpedia, Ruben Verborgh from Ghent University, moderated by Jonathan Holtby from Hub of All Things --- 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…

The Connected Data Podcast

1
Facilitating COVID-19 research with Graph Analytics and Knowledge Graphs 52:33

לפני 5 years52:33

52:33

Scientists, health researchers and policymakers are using all the tools they can get their hands on to try and beat the current global pandemic. Germany’s National Centre for Diabetes Research (DZD) is one of the organizations turning to Artificial Intelligence, advanced visualisation techniques and other tools to aid the search for a vaccine and effective treatments. Graph technology is key in this effort. DZD is integrating data from various sources and linking them in a dedicated COVID-19 Knowledge Graph to help researchers and scientists quickly and efficiently find their way through the more than 40,000 publications out there on the problem. DZD's Head of Data Management and Knowledge Management, Dr. Alexander Jarasch, notes that, “Graph enables a new dimension of data analysis by helping us to connect highly heterogeneous data from various disciplines.” The COVID GRAPH project is a voluntary initiative of graph enthusiasts and companies with the goal to build a knowledge graph with relevant information about the COVID-19 virus. It's a knowledge graph on COVID-19 that integrates various public datasets. This includes relevant publications, case statistics, genes and functions, molecular data and much more. Still, the global scientific knowledge base is little more than a collection of documents. It is written by humans for humans, and we have done so for a long time. This makes perfect sense, after all it is people that make up the audience, and researchers in particular. Yet, with the monumental progress in information technologies over the more recent decades, one may wonder why it is that the scientific knowledge communicated in scholarly literature remains largely inaccessible to machines. Surely it would be useful if some of that knowledge is more available to automated processing. The Open Research Knowledge Graph (ORKG) project is working on answers and solutions. The project, recently initiated, and coordinated by TIB (Leibniz Information Centre for Science and Technology and University Library) is open to the community. ORKG actively engages research infrastructures and research communities in the development of technologies and use cases for open graphs about research knowledge. Dr. Sören Auer, TIB Director and ORKG Lead, states that "Knowledge Graphs..allow us to interlink, interconnect and integrate heterogeneous data from various sources in various formats, modalities, levels of structuredness, governance schemes etc. As a result the effort required for preparing and integrating data for answering specific research questions is dramatically reduced, and AI techniques can more directly applied". Join us as George Anadiotis hosts Alexander Jarasch and Sören Auer in a discussion that will go over: The chronic issues that plague scientific research, and how they apply to life sciences, and SARS-CoV-2 research in specific The way data, analytics, and AI can help deal with the issues and facilitate research The COVID GRAPH project. What is the goal? Who set it up? What does it include? Whom is it for? How does it work? Differences and similarities between property graphs and knowledge graphs, and how that applies in the COVID GRAPH project. Can ORKG and COVID GRAPH work together? What are next steps / outlook? How can people get involved? --- 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…

The Connected Data Podcast

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In Search of the Universal Data Model | Joshua Shinavier 31:33

לפני 5 years31:33

31:33

For as long as people have been thinking about thinking, we have imagined that somewhere in the inner reaches of our minds there are ghostly, intangible things called ideas which can be linked together to create representations of the world around us — a world that has a certain structure, conforms to certain rules, and to a certain extent, can be predicted and manipulated on the basis of our ideas. Rationalist philosophers have struggled for centuries to make a solid case for this intuitive, almost inborn view of human experience, but it is only with the advent of modern computing that we have the opportunity to build machines which truly think the way we think we think. For the first time, we can give concrete form to our mental representations as graphs or hypergraphs, explicitly specify our mental schemas as ontologies, and formally define the rules by which we reason and act on new information. If we so choose, we can even use these human-like building blocks to construct systems that carry far more information than any single human brain, and that connect and serve millions of people in real time. As enterprise knowledge graphs become increasingly mainstream, we appear to be headed in that direction, although there is no guarantee that the momentum will continue unless actively sustained. Where knowledge graphs are likely to be the most essential, in the long run, is at the interface between human and machine; mental representation versus formal knowledge representation. In this talk, we will take a step back from the many practical and social challenges of building large-scale knowledge graphs, which at this point are well-known. Instead, we will take up the quest for an ideal data model for knowledge representation and data integration , seeking common ground among the most popular data models used in industry and open source software, surveying what we suspect to be true of our own inner models, and previewing structure and process in Apache TinkerPop, version 4. We will also take a tentative step forward into the world of augmented perception via graph stream processing. Keynote by Joshua Shinavier , Uber Research Scientist, Apache TinkerPop co-founder, at Connected Data London 2019 Slides available at https://www.slideshare.net/ConnectedDataLondon/keynote-joshua-shinavier-in-search-of-the-universal-data-model-connected-data-london-2019-4 --- 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…

The Connected Data Podcast

1
Graph Databases Will Rule the World in the 2020s. But Why, and How? | Panel Discussion 25:10

לפני 5 years25:10

25:10

Some of us may have been saying that for years, but now the Gartners of the world are picking up on it too. So, the oracles have spoken: “The application of graph processing and graph DBMSs will grow at 100 percent annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science”. That all sounds great, in theory. In practice, however, things are messy. If you’re out to shop for a graph database, you will soon realize that there are no universally supported standards, performance evaluation is a dark art, and the vendor space seems to be expanding by the minute. Recently, the W3C initiated an effort to brings the various strands of graph databases closer together, but it’s still a long way from fruition. So, what’s all the fuss about? What are some of the things graph databases are being used for, what are they good at, and what are they not so good at? Property graphs and RDF are the 2 prevalent ways to model the world in graph; What is each of these good at, specifically? What problems does each of these have, and how are they being addressed? RDF* is a proposal that could help bridge graph models across property graphs and RDF. What is it, how does it work, and when will it be available to use in production? What about query languages? In the RDF world, SPARQL rules, but what about property graphs? Can Gremlin be the one graph virtual machine to unite them all? What about the future of graph databases? Could graph turn out to be a way to model data universally? Moderated by George Anadiotis. Panelists: Geoffrey Horrell Director of Applied Innovation, London Lab at Refinitiv Steve Sarsfield VP of product, Cambridge Semantics Joshua Shinavier Research scientist, Uber --- 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 Data Science: Hybrid AI meets data science process | Paco Nathan