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An introduction to Graph Neural Networks
An introduction to Graph Neural Networks
Neural Networks aimed at effectively handling graph data.Photo by Alina Grubnyak on UnsplashGraph structured data is common across various domains, examples such as molecules, { social, citation, road } networks, are just a few of the vast array of data which can be represented with a graphs. With the advancements of machine learning we witness the potential for applying intelligent algorithms on the data which is available. Graph Neural Network is the branch of Machine Learning which concerns on building neural networks for graph data in the most effective manner.Notwithstanding the progress made with ML in the computer vision domain with convolutional networks, Graph Neural Networks (GNNs) face a more challenging problem, they deal with the awkward nature of graphs. Differently from images and text, graphs do not have a well defined structure. A graph’s node might have no connections or many, of which could be directed or undirected. Graphs in a dataset may have a variable
·towardsdatascience.com·
An introduction to Graph Neural Networks
An introduction to ontology engineering book
An introduction to ontology engineering book
This first general textbook An introduction to ontology engineering has as main aim to provide the reader with a comprehensive introductory overview of...
·linkedin.com·
An introduction to ontology engineering book
Andrea Volpini retweeted: 28% of the 20 million websites we've reviewed are already using Structured Data 🤖 Is yours? Check here: https://t.co/yXBU3nrDg0 https://t.co/ABAOjZ3lpL
Andrea Volpini retweeted: 28% of the 20 million websites we've reviewed are already using Structured Data 🤖 Is yours? Check here: https://t.co/yXBU3nrDg0 https://t.co/ABAOjZ3lpL
28% of 20 million websites reviewed by @woorank are already using Structured Data #connecteddata #knowledgegraph #SEO #semantics #SchemaOrg h/t @cyberandy For a hands-on, in depth tutorial: [LINK]https://www.slideshare.net/ConnectedDataLondon/from-knowledge-graphs-to-aipowered-seo-using-taxonomies-schemas-and-knowledge-graphs-to-improve-search-engine-rankings-and-web
·twitter.com·
Andrea Volpini retweeted: 28% of the 20 million websites we've reviewed are already using Structured Data 🤖 Is yours? Check here: https://t.co/yXBU3nrDg0 https://t.co/ABAOjZ3lpL
Andrea Volpini on Twitter
Andrea Volpini on Twitter
The new language model our teams built is the largest and most powerful one ever created – a milestone with the promise to transform how technology understands and assists us. https://t.co/YvLM0HAr8u— Satya Nadella (@satyanadella) February 12, 2020
·twitter.com·
Andrea Volpini on Twitter
Andrei Kashcha on Twitter
Andrei Kashcha on Twitter
https://t.co/7T0EOs6yG7 - Made this tiny tool to discover related subreddits.The graph is created based on jaccard similarity between two subreddits. Jaccard similarity is constructed from set of shared users.Source code https://t.co/J9r1jl1JjR pic.twitter.com/4hcg7mI4sg— Andrei Kashcha (@anvaka) January 10, 2019
·twitter.com·
Andrei Kashcha on Twitter
Announcing My New Knowledge Representation BookAI3:::Adaptive InformationAI3:::Adaptive Information
Announcing My New Knowledge Representation BookAI3:::Adaptive InformationAI3:::Adaptive Information
Michael K. Bergman announces his new book, A Knowledge Representation Practionary: Guidance from Charles Sanders Peirce. The book applies this guidance to the question of how to best represent human knowledge to computers. The book's practical guidelines should be of interest to any enterprise KM ma
·mkbergman.com·
Announcing My New Knowledge Representation BookAI3:::Adaptive InformationAI3:::Adaptive Information
Announcing Neo4j for Graph Data Science
Announcing Neo4j for Graph Data Science
grade features and scale. We appreciate your candid stories and collaboration, and we’ve used this to create a better solution. As such, we’re excited to announce Neo4j for Graph Data Science™, the first data science environment built to harness the predictive power of relationships for enterprise deployments. Neo4j for Graph Data Science is an ecosystem of tools that includes: With Neo4j for Graph Data Science, data scientists are empowered to confide
·neo4j.com·
Announcing Neo4j for Graph Data Science
Announcing Stardog 7 - Stardog
Announcing Stardog 7 - Stardog
Today we’re happy to announce the GA release of Stardog 7, including new low-level storage engine based on RocksDB. Read on for the glorious details.
·stardog.com·
Announcing Stardog 7 - Stardog
Announcing Stardog 7 beta - Stardog
Announcing Stardog 7 beta - Stardog
We’re happy to announce the beta release of Stardog 7 which comes with a new storage engine that significantly improves write performance.
·stardog.com·
Announcing Stardog 7 beta - Stardog
AnzoGraph DB is now a Graph Database that Supports Geospatial
AnzoGraph DB is now a Graph Database that Supports Geospatial
Use new Geospatial and GeoSPARQL functionality in AnzoGraph DB to develop large scale location intelligence and geospatial applications along-side rich data-analytics using SPARQL* and RDF*.
·blog.cambridgesemantics.com·
AnzoGraph DB is now a Graph Database that Supports Geospatial
Apache Tinkerpop rocks DataStax support for Gremlin - Open Source Insider
Apache Tinkerpop rocks DataStax support for Gremlin - Open Source Insider
Free @DataStax Academy course Getting Started w @apachetinkerpop & Gremlin. To be familiar with Gremlin traversal syntax & techniques, developers need to understand how the language works @ABridgwater @DeniseKGosnell #tutorial #softwaredevelopment
·computerweekly.com·
Apache Tinkerpop rocks DataStax support for Gremlin - Open Source Insider
Apple, Alibaba, Amazon, and the gang promote state of the art in AI and Knowledge Discovery with Graphs | ZDNet
Apple, Alibaba, Amazon, and the gang promote state of the art in AI and Knowledge Discovery with Graphs | ZDNet
In one of the biggest AI events in the world, over 3,000 experts from research and industry showcased and discussed their latest work. Advances in machine learning are happening across the board, and integrating knowledge-based systems with graph-based deep learning promises breakthroughs.
·zdnet.com·
Apple, Alibaba, Amazon, and the gang promote state of the art in AI and Knowledge Discovery with Graphs | ZDNet
ArangoDB Boosts Multi-Model Database Scalability Across Distributed Environments with Release of ArangoDB 3.5
ArangoDB Boosts Multi-Model Database Scalability Across Distributed Environments with Release of ArangoDB 3.5
We are super excited to share the latest upgrades to ArangoDB which are now available with ArangoDB 3.5. With the fast-growing team, we could build many new and long-awaited features in the open-source edition and Enterprise Edition. Get ArangoDB 3.5 on our download page and see all changes in the Changelog. Need to know more […]
·arangodb.com·
ArangoDB Boosts Multi-Model Database Scalability Across Distributed Environments with Release of ArangoDB 3.5
ArangoDB receives Series A Funding led by Bow Capital - ArangoDB
ArangoDB receives Series A Funding led by Bow Capital - ArangoDB
Phew, it’s been quite a ride, but today the whole team is super excited to announce a $10 million Series A funding for ArangoDB, our native multi-model database. We feel honored and frankly a bit proud that Bow Capital is leading this investment round and shows their trust in our product, team, and amazing community. […]
·arangodb.com·
ArangoDB receives Series A Funding led by Bow Capital - ArangoDB