What does graph have to do with machine learning, and data science? A lot, actually, and it goes both ways. Over the last few years, we have seen what was once a niche research topic – graph-based machine learning – snowball. The Year of the Graph was among the first to take stock, point towards this development, and recognize graph-based AI as a key pillar for future development in the field. In this edition of the YotG Newsletter, we highlight resources focused on . Which is not to say there’s lack of news on , , and – on the contrary. graph-based machine learning and data science graph analytics graph databases knowledge graphs 2020 has turned Graph Machine Learning into a celebrity of machine learning, argues Michael Bronstein. Arguably, he’s a celebrity of Graph Machine Learning himself. A pioneering and prolific researcher at Imperial College London, as well as the Head of Graph Learning Research at Twitter. Bronstein sought the opinion of prominent researchers in the field of graph ML and its applications trying to summarise the highlights of the past year and predict what is in store for 2021. For a glimpse of Bronstein’s own work, check his Knowledge Connexions presentation “ ” Deep Learning on Graphs: Past, Present, And Future What 2021 holds for Graph ML? The end of the year is a good time to recap and make predictions. 2020 has turned Graph ML into a celebrity of machine learning. For this post, I sought the opinion of prominent researchers in the field of graph ML and its applications trying to summarise the highlights of the past year and predict what is in store for 2021. Machine learning can help bootstrap and populate knowledge graphs. The information contained in graphs can boost the efficiency of machine learning approaches. A panel featuring some of the world’s top experts in AI, coming from all sides of the spectrum, got together to discuss how . AI + Knowledge are a match made in heaven , , , , , and cover a number of ways graphs, AI and knowledge interact in this 2-hour tour de force. Isabelle Augenstein Nathan Benaich Giuseppe Futia Amy Hodler Katariina Kari Fabio Petroni For the , check our Sergey Ivanov’s quick introduction. Top Applications of Graph Neural Networks 2021 AI + Knowledge – a match made in heaven? 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? Whether you are just getting started with graph-based data science and AI, or you are already advanced, is where you can get both inspiration and hands-on knowledge. Learning with the Machines Aleksa Gordic will share . Bob van Luijt will explain . Paco Nathan will introduce , and then deliver a . How to get started with Graph Machine Learning How businesses apply AI-first solutions in production with the Weaviate Vector Search Engine Graph-Based Data Science hands-on masterclass Tara Safavi will share her work on , and Ashleigh Faith will show how to . CoDEx: A Comprehensive Knowledge Graph Completion Benchmark Add more context to machine learning, using taxonomies and knowledge graphs Learning with the machines Connected Data London Meetup #4 Open source, Graph AI, Search, and Data Science with Python April 15 – 16, 2021 When talking about AI, however, be it graph-based or not, there’s semantics involved. And not just in terms of defining what AI is. AI is not machine learning. Knowledge-based technologies are also AI. Lately, we started seeing more voices advocating for approaches to bridge the worlds of machine learning and knowledge-based technologies. just Gary Marcus, as he shared in a , and in his . Artur d’Avila Garcez and Luis C. Lamb, in their work , Amit Sheth et. al. In . And Frank van Harmelen et. al, who created . series of articles on ZDNet Knowledge Connexions keynote Neurosymbolic AI: The 3rd Wave Semantics of the Black-Box: Can Knowledge Graphs help make Deep Learning systems more interpretable and explainable? Modular Design Patterns for Hybrid Learning and Reasoning Systems Rebooting AI: Deep learning, meet knowledge graphs Gary Marcus, a prominent figure in AI, is on a mission to instill a breath of fresh air to a discipline he sees as in danger of stagnating. Knowledge graphs, the 20-year old hype, may have something to offer there. The last few months have been good for graph databases. A graph database – Neo4j – made the for the 1st time. Neo4j also announced on GCP, preview on AWS. Stardog too. Top 20 in DB Engines general availability of its Aura managed cloud service announced its own cloud DBaaS We had a series of funding rounds, and an upcoming IPO. TigerGraph scored , Katana Graph , and . In the meantime Bitnine, makers of Agens Graph, is – the first in the market. $105M Series C $28.5M Series A Memgraph $6.7M TerminusDB €3.6M working on its IPO Last but not least, we had a round of new releases. to make working with graph databases easier. , , , , , , , and . AWS open-sourced a Graph Notebook Franz Inc released AllegroGraph v7.1 Grakn Labs released Grakn v2.0 Alpha Nebula Graph released v2.0 Ontotext released GraphDB v9.6 RDFox released v.5.0 Stardog released v7.6 TerminusDB released v4.0 From databases to platforms: the evolution of Graph databases Everything you’ve always wanted to ask about graph databases, but did not have the chance to. It’s also been a very active period in terms of book publishing. Whether you’re interested in Graph Databases, Data Analytics on Graphs, or Knowledge Graphs and Semantic Technologies, there’s a new book out there for you. Dave Bechberger and Josh Perryman published . The book introduces graph database concepts by comparing them with relational database constructs. It promises to include just enough theory to get started, then progress to hands-on development. “Graph Databases in Action” on Manning Ljubiša Stanković et.al. . The authors revisit graph topologies from a modern data analytics point of view, and proceed to establish a taxonomy of graph networks. With this as a basis, they show how the spectral analysis of graphs leads to even the most challenging machine learning tasks, such as clustering, performed in an intuitive and physically meaningful way. published “Data Analytics on Graphs” on Now William L. Hamilton published . It aims to serve as If you need a jumping off point to learn more about Graph analytics and the use of graphs in machine learning, as these fields have exploded in the past few years. “Graph Representation Learning” on Morgan & Claypool A Knowledge Representation Practionary Knowledge representation is shorthand for how to represent human symbolic information and knowledge to computers to solve complex questions. KR applications range from semantic technologies and knowledge management and machine learning to information integration, data interoperability, and natural language understanding. Knowledge representation is an essential foundation for knowledge-based AI. AI capabilities are improving daily, but understanding AI’s contextual data and problem-solving approaches is not easy. Graphs are a typically human way of navigating and accessing data. . Graphs and AI bring out the best in the symbiosis of humans and systems To introduce Knowledge Graphs into Organizations you need to assess the right moment, the organization’s semantic maturity, and to overcome overcome segregation/specialization. It’s important to involve the right stakeholders, and . develop KGs in an agile way Gartner: Top 10 data and analytics technology trends for 2021 By 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision making A common use case when working with graphs is converting tabular data to graphs, and adding semantics. From research efforts such as “ “, “ ” and , to and implementations of W3C recommendations, progress is being made. Semantic Annotation for Tabular Data Interactively Constructing Knowledge Graphs from Messy User-Generated Spreadsheets DAGOBAH Linked Data Wizard CSV on the Web csvwlib is a Python implementation of the W3C CSV on the Web recommendations This enables converting tabular data, and optionally its associated metadata, to a semantic graph in RDF or JSON-LD format. Tabular data includes CSV files, TSV files, and upstream may be coming from spreadsheets, RDBMS export, etc. Another domain which historically has been prominent for experiments and . Schema.org had 2 new releases recently, and , summarized by Aaron Bradley and Dan Brickley, respectively. applications of graphs is SEO v.11 v.12 Search engines . Structured markup vocabularies include RDFa & SchemaOrg. JSON-LD is more portable, easier to manage, and it has . encourage content creators & developers to implement structured data recently overtaken Microdata , content creation and digital marketing across the web. The include earning your presence in the Knowledge Graph, as it has tremendous impact across multiple platforms. Knowledge Graphs have huge implications for SEO Top 5 trends for SEO in 2021 SEO Turns to Data Graphs to Learn About the Web Τhe web as a data graph is a new direction for SEO. Many of the articles that people writing about SEO are about to involve web pages and links between pages. Τhis post is about entities and relationships between entities and facts that are written about on web pages, and responses to queries from data graphs on the web about facts and attributes related to entities found on web pages. Journalists, and data journalists, can use Crowdsourcing, Open data and Knowledge Graphs as a data source, by . A new interconnected ecosystem for research is shaping up. leveraging Wikidata Data meets science: Open access, code, datasets, and knowledge graphs for machine learning research and beyond Science and data are interwoven in many ways. The scientific method has lent a good part of its overall approach and practices to data-driven analytics, software development, and data science. Now data science and software lend some tools to scientific research. More use cases for graphs, from exotic to everyday. Research claims an , using machine learning and graphs. AI tool can distinguish between conspiracy theories and true conspiracies for knowledge contained in the content universe. A self-supervised learning task is crafted. Random edges in the graph are selected to form a test set, and the rest of the graph is conditioned to predict missing edges. Netflix uses Knowledge Graphs MIT Machine Learning Uses ‘Graph Grammar’ to Automate and Optimize Robot Design A series of related objects are transformed into new graphs via algorithms, generating new abstractions that adhere to a set of transformational rules Wrapping up with more resources for graph-based machine learning. (pronounced “giraffe”) is a lightweight library for working with graph neural networks in jax. It provides a data structure for graphs, a set of utilites for working with graphs, and a ‘zoo’ of forkable graph neural network models. Jraph : Graph neural networks (GNNs) belong to a category of neural networks that operate naturally on data structured as graphs. Despite being what can be a confusing topic, GNNs can be distilled into just a handful of simple concepts. Graph Neural Networks explained AutoML systems simplify and democratize AI. Tsinghua University developed an AutoML framework & toolkit designed for graph data and tasks. handles all stages of graph learning, may reduce labour & bias in machine learning. AutoGL s Understanding Graph Embedding Graph embeddings have become increasingly important in Enterprise Knowledge Graph strategy. What are they, and how are they related to Mowgli’s Walk? To get the Year of the Graph Newsletter in your inbox, subscribe here Previously published at https://yearofthegraph.xyz/newsletter/2021/04/graph-based-data-science-machine-learning-and-ai-the-year-of-the-graph-newsletter-spring-2021/

The Graph

Funding

Graph Learning News Review: Hot-Topic Facts of Spring 2021 to Take into Account

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

Spend Less on Ads, Get More SEO: A Smarter Growth Play

10 Great Articles On Data Science And Programming!

10 FinTech Trends in 2021 [Part II]

10 Financial Predictions for 2021 to Protect Your Wealth

13 API Metrics That Every Platform Team Should be Tracking

Spend Less on Ads, Get More SEO: A Smarter Growth Play

10 Great Articles On Data Science And Programming!

10 FinTech Trends in 2021 [Part II]

10 Financial Predictions for 2021 to Protect Your Wealth

13 API Metrics That Every Platform Team Should be Tracking

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps