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Finding patterns with rules
Finding patterns with rules
triple store which we will query with SPARQL. If you are not yet familiar with knowledge graphs and reasoning, you can read an introduction published on
·towardsdatascience.com·
Finding patterns with rules
Finding the shortest path from Country A to Country B — using Neo4J and Node
Finding the shortest path from Country A to Country B — using Neo4J and Node
Finding the shortest path from Country A to Country B using Graph #analytics #algorithms #javascript #tutorial #softwareengineering #datascience #GraphDB #opensource #Neo4J #data #tech [LINK]https://technology.amis.nl/2019/01/01/finding-the-shortest-path-from-country-a-to-country-b-using-neo4j-and-node/ [LINK]https://miro.medium.com/max/1370/0*Ol4T6m9f4Y2W7T9W.png
·technology.amis.nl·
Finding the shortest path from Country A to Country B — using Neo4J and Node
Focus: KGs for Food
Focus: KGs for Food
Dan Barber, the James Beard award-winning chef who pioneered the farm-to-table movement, has a single, elegant term for the complexity of molecules, processes, and sequences that underpins great food: flavor. Like great chefs, computer scientists grapple with complexity, seeking elegant ways to model and analyze increasingly complex–or perhaps, flavorful–phenomena. Across many business verticals, we are […]
·knowledgegraph.tech·
Focus: KGs for Food
Football meets graphs
Football meets graphs
FOOTBALL MEETS GRAPHS Bea Hernández
·docs.google.com·
Football meets graphs
For those of you who missed it - here is the recording of my webinar at @Dataversity on #graph #datamodeling - summary of my #online #training at #Dataversity: lnkd.in/dK6ex9F
For those of you who missed it - here is the recording of my webinar at @Dataversity on #graph #datamodeling - summary of my #online #training at #Dataversity: lnkd.in/dK6ex9F
For those of you who missed it - here is the recording of my webinar at @Dataversity on #graph #datamodeling - summary of my #online #training at #Dataversity: https://t.co/3p71i3kGYP— Thomas Frisendal (@VizDataModeler) January 11, 2020
·twitter.com·
For those of you who missed it - here is the recording of my webinar at @Dataversity on #graph #datamodeling - summary of my #online #training at #Dataversity: lnkd.in/dK6ex9F
Formula for Success
Formula for Success
László Barabási never bothered to learn English. “My worst grades were always in English because I thought, Why study it? You can never leave this country,” explains Barabási, director of the Center for Complex Network Research (CCNR) at Northeastern University in Boston. “It wasn’t until I got to the University of Bucharest and became interested in research that I understood the importance of being able to read academic papers in English.” Barabási emigrated from Romania to Budapest with his father in the summer of 1989, a few months before Ceaușescu was overthrown, and completed a master’s degree in physics at Eötvös Loránd University two years later. But it wasn’t until after he’d earned a Ph.D. in physics at Boston University in 1994, while working as a postdoc at IBM’s legendary Thomas J. Watson Research Center, that Barabási became inter
·weareworldquant.com·
Formula for Success
Francis Opoku on Twitter: "Short comparision of #SPARQL and #GraphQL by @RubenVerborgh. One SPARQL Query can go against multiple SPARQL endpoints in a natural way - that is not equal to schema stitching in GraphQL because there is no need for schema assum
Francis Opoku on Twitter: "Short comparision of #SPARQL and #GraphQL by @RubenVerborgh. One SPARQL Query can go against multiple SPARQL endpoints in a natural way - that is not equal to schema stitching in GraphQL because there is no need for schema assum
Short comparision of #SPARQL and #GraphQL by @RubenVerborgh. One SPARQL Query can go against multiple SPARQL endpoints in a natural way - that is not equal to schema stitching in GraphQL because there is no need for schema assumptions of different #API's: https://t.co/RWYuvBSIqX— Francis Opoku (@fraopo) December 23, 2018
·twitter.com·
Francis Opoku on Twitter: "Short comparision of #SPARQL and #GraphQL by @RubenVerborgh. One SPARQL Query can go against multiple SPARQL endpoints in a natural way - that is not equal to schema stitching in GraphQL because there is no need for schema assum
Frank van Harmelen on Twitter
Frank van Harmelen on Twitter
A big new #LOAD resource: https://t.co/J44j8EKsGo All 500M+ owl:sameAs statements in the #LOD cloud, their closure into 50M equivalence classes, and an estimated error degree for all of these. All for download at https://t.co/sKeetxXzgY— Frank van Harmelen (@FrankVanHarmele) April 28, 2019
·twitter.com·
Frank van Harmelen on Twitter
Functional Knowledge Graph Community Group
Functional Knowledge Graph Community Group
The mission of FKG group is to create specifications for encoding ontologies that AI Assistants can operate upon enabling them to execute functions embedded in a web page. FKGs are encoded in JSON-LD, this group defines the vocabulary. A detailed proposal can be found at: https://github.com/keyvan-m-sadeghi/assister/blob/assister-conception/rfcs/text/assister-conception/README.md
·w3.org·
Functional Knowledge Graph Community Group
Gartner Identifies Top 10 Data and Analytics Technology Trends for 2019
Gartner Identifies Top 10 Data and Analytics Technology Trends for 2019
Augmented analytics, continuous intelligence and explainable artificial intelligence (AI) are among the top trends in data and analytics technology that have significant disruptive potential over the next three to five years, according to Gartner, Inc.
·gartner.com·
Gartner Identifies Top 10 Data and Analytics Technology Trends for 2019
Generalized Language Models
Generalized Language Models
As a follow up of word embedding post, we will discuss the models on learning contextualized word vectors, as well as the new trend in large unsupervised pre-trained language models which have achieved amazing SOTA results on a variety of language tasks.
·lilianweng.github.io·
Generalized Language Models
George Cushen: Knowledge graphs --enter- the Hype Cycle | PyData London 2019 - YouTube
George Cushen: Knowledge graphs --enter- the Hype Cycle | PyData London 2019 - YouTube
So you have heard the hype about knowledge graphs? How can Pythonistas join forces with Fashionistas to form an authoritative single source of truth for fashion e-commerce, benefiting applications such as Search, Discovery, and Personalisation. This talk will give you a unique insight into how knowledge graphs can provide powerful ways to analyse and emphasise relationships in data. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analys...
·youtube.com·
George Cushen: Knowledge graphs --enter- the Hype Cycle | PyData London 2019 - YouTube