GraphNews

Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning Jiang et al.: https://lnkd.in/dSSsm-d #ArtificialIntelligence ...

How to combine Named Entity Linking with Wikipedia data enrichment to analyze the internet news

Wikidata can be a useful resource for journalists digging for data on a deadline. Here is a guide to using the community-edited database as a data source.

Artificial Intelligence (AI) has made enormous strides in the last several years both due to the advent of technologies such as deep…

In this week's #twin4j, @tb_tomaz create a news monitoring data pipeline using Natural Language Processing and a Knowledge Graph.https://t.co/mIsm7Uj4iL #neo4j pic.twitter.com/XSQsQYhNzf— Neo4j (@neo4j) February 7, 2021

Working on part3 of #Wordnet in #Neo4jhttps://t.co/5qcL0LWtoB#python + #nltk + cypher for "similar phrase generation" following graph pattern entry>sense>concept and extending it w. hyper/hyponym to make general/specificmore specific variants of data? metadata,raw data...👌 pic.twitter.com/BMcOPbaub3— Jesús Barrasa (@BarrasaDV) February 8, 2021

One of the great things about the database market is that there are many different kinds of data and the problems that need to be addressed to store,

"CHOLAN: A Modular Approach for Neural Entity Linking on @Wikipedia and @Wikidata", a pipeline of two transformer-based models integrated sequentially for end-to-end entity linking.(Kannan Ravi et al, 2021)paper: https://t.co/VD9uyxc0RScode/data: https://t.co/BZYG5XGPO5 pic.twitter.com/aqLPDopXAb— WikiResearch (@WikiResearch) February 8, 2021

Marvel gangs: Nice network graph of links among #MCU heroes. Six gangs centered on #CaptainAmerica (most central in MCU), #Thor , #SpiderMan , #HULK, #FantasticFour and X-Men. Lots of overlap, especially between Cap, Tony Stark and Thor. Data at Kaggle. https://t.co/v5uqSAaIxK pic.twitter.com/xDfBEQS6og— Dan Armstrong (@Fuertebrazos) February 9, 2021

OntoEnricher: A Deep Learning Approach for Ontology Enrichment from Unstructured Text https://t.co/az22OFHYf7 pic.twitter.com/sUpamyvxyw— Aaron Bradley (@aaranged) February 9, 2021

A Framework for Federated SPARQL Query Processing over Heterogeneous Linked Data Fragments https://t.co/oGdCVjTCyv pic.twitter.com/fL2bmDIgN4— Aaron Bradley (@aaranged) February 9, 2021

Linked Data projects at the Vrije Universiteit Amsterdam Network Institute @dbpedia https://t.co/uYNpEMGn0Y— Aaron Bradley (@aaranged) February 9, 2021

Over 28,000 peer-reviewed scientific papers about graph-powered data science have been published in recent years - Read more here!

SEO chapter of the 2020 Web Almanac covering content, meta tags, indexability, linking, speed, structured data, internationalization, SPAs, AMP and security.

The emergent integrated network structure of scientific research "In this study, [they] propose a topic network framework for investigating the emergent...

Deep learning update: What have I learned about Graph ML in 2 months?

Last week, I was discussing the key features of an Enterprise Knowledge Graph (EKG) with some colleagues, and I realized that although we…

We’re excited to announce a collaboration that we’ve been working on with the wonderful team at arxiv.org.

Connected Papers are now partnered with arXiv.org and from now on every paper page in arXiv will link to a corresponding Connected Papers graph! Check...

Patterns for Representing Knowledge Graphs to Communicate Situational Knowledge of Service Robots https://t.co/xktHqliz8q pic.twitter.com/yZgiSFgQeI— Aaron Bradley (@aaranged) January 28, 2021

The English WordNet in #Neo4j. https://t.co/buXxA8OdqX #NLP #KnowledgeGraphs pic.twitter.com/MY0EAaRDon— Graphs & Networks (@TheOrbifold) February 1, 2021

"Towards a Systematic Approach to Sync FactualData across #Wikipedia, #Wikidata and ExternalData Sources"(Hellmann et al, 2021)paper: https://t.co/TlYkPg5jg6tool: https://t.co/YPVhcgP8W7 pic.twitter.com/bSnkPOmHeU— WikiResearch (@WikiResearch) February 1, 2021

"GovData goes #SPARQL. From now on, queries on the original metadata are possible via a triple store endpoint:" https://t.co/eDvPCxgriO https://t.co/lvlagMyzsC— Learning SPARQL (@LearningSPARQL) January 30, 2021

Graph analysis using the tidyverse.https://t.co/PIIN0UFlzH #rstats #graphs pic.twitter.com/cs7ynHD9rh— Graphs & Networks (@TheOrbifold) February 2, 2021

Today is the day! #BabelNet 5 is out! https://t.co/ybsOc0WNmDNew interface, up-to-date content in 500 languages, 20 million synsets, WordNet 2020, and much more! Thanks to the great team behind this fantastic release! #knowledgegraphs #multilinguality @SapienzaNLP @Babelscape pic.twitter.com/5rCIEAixD0— Roberto Navigli (@RNavigli) February 2, 2021

A Knowledge Graph for the Agri-Food Sector @rapw3k https://t.co/Uko5uhcg2F— Aaron Bradley (@aaranged) February 1, 2021

"Each instance of ResearchSpace has at its core a dynamic and expandable graph-based representation of networks of people, things, places, and events, a structure we refer to as the knowledge graph." https://t.co/oab1VKFPVd— Aaron Bradley (@aaranged) February 1, 2021

Dynamic graphs are a big part of how Twitter does what it does. We use them to model networks that evolve over time. In this post @emaros96 & @mmbronstein discuss a new ML model developed by Twitter to efficiently predict activity in dynamic graphs.https://t.co/BKk0BBTAk0— Twitter Engineering (@TwitterEng) February 1, 2021