GraphNews

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Apply web scraping bots , computational linguistics, and natural language processing algorithms to build knowledge graphsContinue reading on Towards Data Science »

Wikipedia2Vec: #Python #opensource tool for learning word & entity embeddings from #Wikipedia. Demo: https://t.co/Gv5EBXWbuX #Research paper: https://t.co/GGbQjQolJe #datascience #AI #NLP h/t @aaranged

Facilitating #journalism #automation via #knowledgegraphs. KG nodes corresponding to news articles, arrows show their connections. Generated using @sentilecto_NLU, allows navigating the spacial representation of a set of related texts #AI h/t @aaranged

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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

New from Google Research! REALM: https://t.co/kS2oTyxAAjWe pretrain an LM that sparsely attends over all of Wikipedia as extra context. We backprop through a latent retrieval step on 13M docs. Yields new SOTA results for open domain QA, breaking 40 on NaturalQuestions-Open! pic.twitter.com/DYDFX69Td8— Kelvin Guu (@kelvin_guu) February 11, 2020

This year, the @UGent Web Development course kicks off with a very special guest: @timberners_lee introduces to our students his invention that changed the world. Not “vague but exciting”—rather crystal clear and as passionate as ever. https://t.co/E4JQH3bFfM

This is great, and don’t miss the other ontologies at this excellent subdomain name.

Our physical embedding model for #knowledgegraphs achieve quasi-linear scalability. Check out the video by @_CaglarDemir at https://t.co/9n8tkZoWXg #MachineLearning #OpenScience @knowgraphs— Axel Ngonga (@NgongaAxel) February 14, 2020

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"... the first version of Meena reportedly has 2.6 billion parameters and is trained on 341 GB of text, filtered from public domain social media conversations." @jrdothoughts bit.ly/2UXJHYF

A "definitive feature of knowledge graphs is the ability to connect concepts to external resources using Linked Data". ✔️ @taxobob bit.ly/2SDis4h

If you’re one of the holdouts who still has a Facebook account you’ll be happy to know that Big Zuck now lets you control — and delete —…Continue reading on Medium »

The growing interest in making use of Knowledge Graphs for developing

SourceThe year 2020 has just started but we can already see the trends of Graph Machine Learning (GML) in the latest research papers. Below is my view on what will be important in 2020 for GML and the discussion of these papers.The goal of this article is not on introducing the basic concepts of GML such as graph neural networks (GNNs), but on…

At linked data-driven companies you will find it difficult to distinguish between “traditional” data workers and those in other functional areas who, at other companies, are less reliant on data.

This article shows how an RDF Graph CRUD application can be rapidly developed, yet without losing the flexibility that HTML5/JavaScript offers, from which it can be concluded that there is no reason preventing the use of RDF Graphs as the backend for production-capable applications.

Overview of the research domain and works of the joint research team Wimmics (UCS, Inria, CNRS, I3S) in Sophia Antipolis, France

Prelude In December, I wrote a Quora post on the pros and cons of graph databases. I shared two cons pervasive in the market today: the difficulty of finding proficient graph developers, and how non-standardization on a graph query language is slowing down enterprise adoption,...

Graph machine learning finds dissatisfied customer cohorts an recommends optimal intervention measures.

Massively parallel implementation of #Graph2Vec = scalable graph representation #algorithm learns vectors that describe whole graphs in an embedding space:https://t.co/nR0Iv2QG4u by @benrozemberczki#BigData #DataScience #AI #MachineLearning #LinkedData #GraphDB #GraphAnalytics pic.twitter.com/7INPwfeDPw— Kirk Borne (@KirkDBorne) August 25, 2019

An evaluator's task is to connect the dots between program goals and its outcomes. This can be accomplished through surveys, research, and interviews, and is frequently performed post hoc. Research evaluation is hampered by a lack of data that clearly connect a research program with its outcomes and, in particular, by ambiguity about who has participated in the program and what contributions they have made. Manually making these connections is very labor-intensive, and algorithmic matching introduces errors and assumptions that can distort results. In this paper, we discuss the use of ident...

I just published the latest version of Practical Gremlin in all supported formats. Another substantial update. Please see the change history for full details. https://t.co/UNJzfsUg3s … … https://t.co/mXGaOEe3q6 … … https://t.co/7YtyD2xQuR … … @apachetinkerpop @JanusGraph pic.twitter.com/EcokGbuwMN— Kelvin Lawrence (@gfxman) May 29, 2018

What is an application network? And why does it need a graph? MuleSoft's founder and CTO help explain today's announcement at Dreamforce 2018

We’re pleased to announce the start of a multi-part series of posts for Amazon Neptune in which we explore graph application datasets and queries drawn from many different domains and problem spaces. Amazon Neptune is a fast and reliable, fully-managed graph database, optimized for storing and querying highly connected data. It is ideal for online […]

Machine learning algorithms don’t yet understand things the way humans do — with sometimes disastrous consequences.