The Connected Data Podcast

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

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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

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.

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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

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.

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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

"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

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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

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.

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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.

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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

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

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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

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/

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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

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

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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

“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.

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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).

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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

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

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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