AI + Knowledge - a match made in heaven?

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About this talk

What does graph have to do with machine learning?

A lot, actually. And it goes both ways

Machine learning can help bootstrap and populate knowledge graphs.

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

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

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

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

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

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

Kindly sponsored by:

<img alt="Neo4j is the leader in graph database technology and the world’s most widely deployed graph database. We help brands – including Comcast, NASA, UBS &amp; Volvo – to reveal and predict how people, processes, and systems are interrelated." class="b-lazy img-fluid" data-src="" height="" src="data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==" width="300">

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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


  • 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?


  • 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


  • Intermediate - Advanced

Prerequisite Knowledge

  • Basic understanding of Knowledge Graphs
  • Basic understanding of Machine Learning / Deep Learning

Categories covered by this talk

Isabelle Augenstein

Isabelle is an Associate Professor at the University of Copenhagen, where she heads the Copenhagen Natural Language Understanding research group. Her main research interests are weakly supervised and low-resource learning with applications including information extraction, machine reading and fact checking.

Nathan Benaich

General Partner of Air Street Capital, a venture capital firm investing in AI-first technology and life science companies. He founded RAAIS and London.AI, which connect AI practitioners from large companies, startups and academia, and the RAAIS Foundation that funds open-source AI projects. He studied biology at Williams College and earned a PhD from Cambridge in cancer research.

Amy Hodler

Amy Hodler is the Graph Analytics & AI program director at Neo4j. She loves seeing how the community uses graph analytics to reveal structures within real-world networks and infer behavior. Amy is the co-author of the O'Reilly book Applied Graph Algorithms in Apache Spark and Neo4j, published in early 2019 and updated July 2020.

Katariina Kari

Katariina Kari (née Nyberg) is a data engineer at Zalando. 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.

Fabio Petroni

AI & Deep Learning Enthusiast. Broad areas of interest include natural language processing and machine learning in general. Currently studying natural language processing models in the Facebook Artificial Intelligence Research (FAIR) lab in London. Previously with the R&D department at Thomson Reuters.

Giuseppe Futia

Research Scientist and Knowledge Graph Engineer at the Nexa Center for Internet and Society. He received the Ph.D. degree in Computer Engineering from the Politecnico di Torino in September 2020. His research interests focus on semantic modeling, data integration, and Graph Neural Networks.

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