Very nice article highlighting recent proof that complete graphs can be decomposed into smaller multiple trees. twitter.com/QuantaMagazine…

world relationships to be recorded and analysed without losing information. Questions can then be asked of the data, such as the strength and direction of relationships between objects in the graph. Graphs are mathematical structures utilised to model numerous forms of relationships and processes in information

up Lisp development style that I have used for decades using symbolic AI and knowledge representation."

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

"... 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

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.

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

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

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

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 […]

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

Discover a new way to visualize and explore your connected data with SemSpect: a unique approach to graph visualization that doesn't depend on using random or best-guess Cypher queries in order to explore your data's meta-graph and that is compatible with Neo4j (including RDF datasets).

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...

Graphs are a general-purpose #datamodel, as relational was a general-purpose #data model a generation ago. A supply chain is a graph. Knowledge is a graph. Graphs are very applicable in a wide range of use cases @jimwebber @TheRegister #GraphDB #tech [LINK]https://www.theregister.co.uk/2020/02/05/graph_database_neo4j_chief_scientist/ [LINK]https://regmedia.co.uk/2016/04/26/graph_database.jpg

Representing Graph #Data Structures: Edge Lists, Adjacency Matrices, Adjacency Lists #datascience #analytics #tutorial #datamodel #softwareengineering #data #tech

#Neo4j #GraphDB has added sharding @adamcowley shows when and how to use it #softwareengineering #data #tech #opensource #tutorial [LINK]https://adamcowley.co.uk/neo4j/sharding-neo4j-4.0/ [LINK]https://neo4j.com/docs/operations-manual/4.0-preview/images/fabric-single-instance.png

The most depicted humans in #paintings #SPARQL query on #Wikidata looks an #artwork itself https://t.co/MgWi4bCDPo (ht @sanseveria ) pic.twitter.com/qMjZtNkCFM— Maarten Dammers (@mdammers) January 18, 2020

Property graphs seem inherently simpler. Semantic graphs put more emphasis on #datamodeling, treat node/edges as concepts bound by a universally unique identifier #data #tech #knowledgegraph #EmergingTech #datamodel #datascience #software @kurt_cagle [LINK]https://www.linkedin.com/pulse/state-graph-merger-property-graphs-semantic-kurt-cagle [LINK]https://media-exp1.licdn.com/dms/image/C5612AQF2DIFR7DjXKQ/article-cover_image-shrink_600_2000/0?e=1585785600&v=beta&t=tN9Z8ZbE1VeNHiPvhdFfDi34VwHW_pOHXPWv7Il8f7Q

An intro to querying #linkeddata using SPARQL & @Wikidata. Which films starred more than one future head of government? Largest city with a female mayor? Questions #linkeddata can answer #opendata #knowledgegraph #datascience @CCC h/t @LearningSPARQL [LINK]https://media.ccc.de/v/36c3-60-querying-linked-data-with-sparql-and-the-wikidata-query-service

#SchemaOrg 6.0 is out. Lots of small but useful improvements. New types Guide, Recommendation may be of interest for #digitalmarketing h/t @aaranged @danbri #SEO #semantics #knowledgegraph #search [LINK]https://schema.org/docs/releases.html#v6.0 [LINK]

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

.@CamSemantics announces Anzo #GraphDB v.2: RDF*, Custom SDK, Free Edition. "Imagine being able to do labeled properties, just like you do in Neo4j & other property graphs, but also have capability of RDF to help w ontologies & inferencing" #data #tech

.@NebulaGraph is a new #opensource #GraphDB, claiming to be capable of hosting super large scale graphs w/ dozens of billions of nodes, trillions of edges, milliseconds of latency. Inspired by #Facebook project Dragon, featured on page #1 on @HackerNews

Creating Graphs in Python using Networkx #dataviz #tutorial #datascience #data #tech

Graph Theory and #DataScience: A topic intro with the Bridges of Königsberg. Graph Theory can be used to represent and analyze a wide variety of network information, has numerous modern applications within Data Science #data #tech #software #algorithms

#Semantic #technology, decision intelligence, knowledge #datascience will be our companions in the next years, so it's recommended to start exploring #graphdatabases, #ontologies, knowledge representation systems #knowledgegraph #AI #2020NewYear #trends