Engineering Content for Superior Search Performance: Introducing Structured Data
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Using #Ontologies is essential in successfully training #MachineLearning data sets. Read our blog from our CTO James Malone as he discusses how Sherlock Holmes made an appearance when we extracted #LifeScience articles from Wikipedia scibite.com/news/a-se
semanti… https://t.co/d0FvOQ2HlK
“Data is the raw material for Industry 4.0 and a prerequisite for optimizing production with the help of artificial intelligence. At OMP, we are developing a semantic model that makes data understandable and illustrates its relations and dependencies." --
Michael Bolle of Bosch.
"Gremlin++ & BitGraph: Implement the Gremlin Traversal Language and A GPU-Accelerated Graph Computing Framework in C++" drum.lib.umd.edu/handle/1903/21… see Gremlin++ code here: github.com/bgamer50/Greml… #graphdb https://t.co/LmSsABE7SW
Accelerated Graph Computing Framework in C++" drum.lib.umd.edu/handle/1903/21… see Gremlin++ code here: github.com/bgamer50/Greml… #graphdb https://t.co/LmSsABE7SW
An approach for semantic integration of heterogeneous data sources
enterprise context, the problem arises of managing information sources that do not use the same technology, do not have the same data representation, or that have not been designed according to the same approach. Thus, in general, gathering information is a hard task, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure are unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. Over the years, several data integration solutions have been proposed:
"name": "Sherman McCoy", "jobTitle": "Master of the Universe" Quoted tweet from @glenngabe: That's interesting -> Google Search to introduce public profile cards for all, a replacement for G+ profiles "The new Google Search profile cards appear to be diff
"name": "Sherman McCoy",
* SketchGCN: Semantic Sketch Segmentation with Graph Convolutional Networks (arxiv.org/abs/2003.00678) #cvpr #cvpr20 #gml
* SketchGCN: Semantic Sketch Segmentation with Graph Convolutional Networks (arxiv.org/abs/2003.00678)
“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.”
How to Visualize Social Network With Graph Theory
And Find out who are the most Influencers in Game of ThronesContinue reading on Towards Data Science »
Knowledge representation and update in hierarchies of graphs. (arXiv:2002.01766v1 [cs.AI])
A mathematical theory is presented for the representation of knowledge in the
Victor Lee posted on LinkedIn
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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
Chidambara .ML. retweeted: Graph Neural Networks #BigData #deeplearning #learning via twinybots.ch theaisummer.com/Graph_Neural_N…
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 on Twitter
https://t.co/4Ohse28toR
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."
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
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
Ruben Verborgh on Twitter
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
A "definitive feature of knowledge graphs is the ability to connect concepts to external resources using Linked Data". ✔️ @taxobob bit.ly/2SDis4h
A "definitive feature of knowledge graphs is the ability to connect concepts to external resources using Linked Data". ✔️ @taxobob bit.ly/2SDis4h
Really Rapid RDF Graph Application Development
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.
Dreamforce 2018 - explaining MuleSoft's application network graph
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
Expero Blog | Customer Satisfaction Scoring Using GraphML
Graph machine learning finds dissatisfied customer cohorts an recommends optimal intervention measures.
Kelvin Lawrence on Twitter
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
Let Me Graph That For You – Part 1 – Air Routes
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 […]