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What does graph have to do with machine learning?
A lot, actually. And it goes both ways
Machine learning can help bootstrap and populate knowledge graphs.
The information contained in graphs can boost the efficiency of machine learning approaches.
Machine learning, and its deep learning subdomain, make a great match for graphs. Machine learning on graphs is still a nascent technology, but one which is full of promise.
Amazon, Alibaba, Apple, Facebook and Twitter are just some of the organizations using this in production, and advancing the state of the art.
More than 25% of the research published in top AI conferences is graph-related.
Domain knowledge can effectively help a deep learning system bootstrap its knowledge, by encoding primitives instead of forcing the model to learn these from scratch.
Machine learning can effectively help the semantic modeling process needed to construct knowledge graphs, and consequently populate them with information.
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Check them outIsabelle 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.
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 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 (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.
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.
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.