Identifying Ingredient Substitutions Using a Knowledge Graph of Food
Identifying Ingredient Substitutions Using a Knowledge Graph of Food https://t.co/5pZWc9Bt94 pic.twitter.com/PgkMr9idHj— Aaron Bradley (@aaranged) March 3, 2021
The Rise of the Knowledge Scientist - Data Science Central
The still young discipline of the management and governance of knowledge graphs (KG) is gradually beginning to consolidate on the basis of concrete project ex…
[2102.11965] Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases
The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognised as one of the key challenges of modern AI. Recent years have seen large number of...
A curated list of various semantic web and linked data resources
https://lnkd.in/dTvZzMb "A curated list of various semantic web and linked data resources. To add something to the list please either submit a pull request...
160 SPARQL endpoints, indexing of stuff in GitHub repositories: interesting work by Vincent Emonet at @UM_IDS: https://t.co/6Jh93YEvRP (file PRs for missing resources here: https://t.co/M5d94NgIrn)— Egon Willighⓐgen (@egonwillighagen) February 27, 2021
From Knowledge Graphs to Knowledge Workflows | Diffblog
2020 Was The “Year of the Knowledge Graph” 2020 was undeniably the “Year of the Knowledge Graph.” 2020 was the year that Gartner put Knowledge Graphs at the peak of its hype cycle. It was the year where 10% of the papers published at EMNLP referenced “knowledge” in their titles. It was the year over […]
QuickGraph#18 Semantic similarity metrics in taxonomies: A wikipedia example on uncrewed spacecraft – Jesús Barrasa
In this post i’ll give you an overview of some similarity metrics I’ve discovered when working with WordNet. Even though they were originally proposed as linguistic similarity metrics, …
Graph Convolutional Embeddings for Recommender Systems
"Graph Convolutional Embeddings for Recommender Systems" by Paula Gómez Duran, Alexandros Karatzoglou et al. Paper: https://lnkd.in/dHskknV #graphnn #...
new life sciences SPARQL endpoint. License unclear, and contains (with permisison) some proprietary data (MeSH): https://t.co/HuvlSNhRo3 https://t.co/gSFH7o7Oxo— Egon Willighⓐgen (@egonwillighagen) February 25, 2021
https://t.co/ooIuC1elTy version 12 is out - thanks to all who collaborated on this! https://t.co/rCzFkiwbBo has the details. In this edition https://t.co/ooIuC1elTy can now distinguish 6 kind of media-authenticity problem for reviewing images and videos; ... pic.twitter.com/a6yZv0ayDR— Dan Brickley (@danbri) March 8, 2021
Network mapping is a concrete method to include more voices in your reporting » Nieman Journalism Lab
It gives a framework and place to begin, recognizing that no outreach plan will work for everyone so it’s necessarily an iterative, step-by-step process.
New @orkg_org release today featuring a new contribution editor, where you can directly add a comparison table to capture the state-of-the-art for a particular research problem: https://t.co/nhZ7Gjzilh pic.twitter.com/MJmLp9bvIi— Sören Auer (@SoerenAuer) March 8, 2021
“This library provides a prolog interface to SPARQL queries. It allows logic program queries to be compiled to SPARQL, and then executed on a remote SPARQL server.” https://t.co/3YaKL1AJkq— Learning SPARQL (@LearningSPARQL) March 8, 2021
GNN is the new hype but revisiting deep learning applied to graphs is a great opportunity to learn some fundamental concepts in machine learning: language...