“Aspen is a simple markup language for creating graph data.” Quoted tweet from @mesirii: Just came across Aspen by Matt Cloyd Really cool syntax for expressing graphs and transformation to #Neo4j Cypher aspen-lang.org thanks @mdavidallen
“Aspen is a simple markup language for creating graph data.”
Cf. this discussion on the #schema.org mailing list kicked off by this post's author lists.w3.org/Archives/Publi… Quoted tweet from @datao: blog.sparna.fr/2020/02/20/sem…
Cf. this discussion on the #schema.org mailing list kicked off by this post's author lists.w3.org/Archives/Publi…
Introducing the Neo4j Graph Data Science plugin with examples from the “Graph Algorithms…
Introducing the Neo4j Graph Data Science plugin with examples from the “Graph Algorithms: Practical Examples in Apache Spark and Neo4j” bookIn the past couple of years, the field of data science has gained much traction. It has become an essential part of business and academic research. Combined with the increasing popularity of graphs and graph databases, folks at Neo4j decided to release a Graph Data Science (GDS) plugin. It is the successor of the Graph Algorithms plugin, that is to be deprecated.Those of you who are familiar with Graph Algorithms plugin will notice that the syntax hasn’t changed much to allow for a smoother transition. To show what has changed, I have prepared the migration guides in the form of Apache Zeppelin notebooks that can be found on GitHub.Neo4j connector for Apache Zeppelin was developed by Andrea Santurbano, who also designed the beautiful home page notebook of this project and helped with his ideas. In the migrations guides, we used the ex
The Property Graph features are included for free in every edition of the @OracleDatabase - here's what's new in 20c: blogs.oracle.com/oraclespatial/… #oraclegraph #Analytics #DataScience https://t.co/hXlgPgxcxJ
here's what's new in 20c: blogs.oracle.com/oraclespatial/… #oraclegraph #Analytics #DataScience https://t.co/hXlgPgxcxJ
Aaron Bradley retweeted: To help promoting the usage of @wikidata , here's another attempt to explain properties of statements with one of the most used qualifiers "start time" (pq:P850). Try it: https://t.co/tvOeWK7qBM #SPARQL #LinkedData https://t.co/Rs
To help promoting the usage of @wikidata , here's another attempt to explain properties of statements with one of the most used qualifiers "start time" (pq:P850).Try it: https://t.co/tvOeWK7qBM#SPARQL #LinkedData pic.twitter.com/RshdPRx92D— Ivo Velitchkov (@kvistgaard) February 20, 2020
Very nice article highlighting recent proof that complete graphs can be decomposed into smaller multiple trees. twitter.com/QuantaMagazine… Quoted tweet from @QuantaMagazine: Mathematicians have proved a 60-year-old problem in combinatorics called Ringel’
Very nice article highlighting recent proof that complete graphs can be decomposed into smaller multiple trees. twitter.com/QuantaMagazine…
How contextual monitoring using graph analytics can improve your data insights
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
Congratulations Mark! I like this: "the Hy language ... offers transparent access to Python Deep Learning frameworks with a bottom-up Lisp development style that I have used for decades using symbolic AI and knowledge representation." Quoted tweet from @m
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
Diego Moussallem retweeted: New from Google Research! REALM: realm.page.link/paper We 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 do
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
"... 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
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.
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 […]
Meet SemSpect: A Different Approach to Graph Visualization [Community Post]
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).
Using ORCID, DOI, and Other Open Identifiers in Research Evaluation
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...
Whaddya mean, 'niche'?! Neo4j's chief scientist schools El Reg on graph databases • The Register
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
#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
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