A Gateway to Knowledge Graphs
Earlier this month I asked here on LinkedIn about interest publishing a short piece on Knowledge Graphs. The response was overwhelmingly positive, so today...
TinkerPop on Twitter
G.V() is an all-in-one Gremlin IDE to write, test and analyze results for your TinkerPop-enabled graph database - in open beta now and looking for feedback: https://t.co/0FLK7kC9K9 #graphdb pic.twitter.com/7U8PeKFsm9— TinkerPop (@apachetinkerpop) November 22, 2021
Making FastRP graph embeddings work for you
How to tune the hyperparameters of FastRP to your specific problem
Graph Neural Networks through the lens of Differential Geometry and Algebraic Topology
New perspectives on old problems in Graph ML
Knowledge Graphs
At its core: How’s a graph database different from a relational?
Beyond listing superficial benefits and implications: Let’s explore what the fundamental difference of the two is.
How To Create Content Hubs Using Your Knowledge Graph
Learn more about content hubs and how to build them by leveraging deep learning and data in a knowledge graph and using a specific technique called knowledge graph embeddings (or simply KGE).
Knowledge Graphs @ EMNLP 2021
Your regular digest of KG research, November edition
Intel® Labs’ Graph Neural Networks Research Featured as Groundbreaking Work in AI
By Matthias Müller, Head of Embodied AI Lab at Intel Labs Matthias Mueller leads the Embodied AI Lab at Intel Labs, where he and his team develop algorithms pertaining to the fields of robotics, computer vision, graphics, and machine learning. The goal of Intel Labs’ Embodied AI Lab is to build inte
Ex-Google CFO Patrick Pichette joins $2 billion database startup Neo4j's board: 'The scope for applications is incredible.'
Swedish unicorn Neo4j counts the likes of Adobe, AstraZeneca, Comcast, Ebay, and UBS among its growing clientele.
Building (up) GraphNNs!
Sometime I become so addicted to a particular framework that I completely forget about how the world is changing out there. Therefore…
Papers with Code - Papers with Code Newsletter #19
Papers With Code highlights trending Machine Learning research and the code to implement it.
How to transfer algorithmic reasoning knowledge to learn new algorithms?
Learning to execute algorithms is a fundamental problem that has been widely studied. Prior work~\cite{veli19neural} has shown that to enable systematic generalisation on graph algorithms it is...
Cryptocurrencies Activity as a Complex Network: Analysis of...
The number of users approaching the world of cryptocurrencies exploded in the last years, and consequently the daily interactions on their underlying distributed ledgers have intensified. In this...
Graph Representation Learning — Objective Functions and Encoders (Part 3)
Let’s understand how to optimize GRL models and their ability to incorporate different network features
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Apollo GraphQL expands federation, adopts Elastic license | ZDNet
Can GraphQL scale beyond workgroups or departments? Changes to Apollo GraphQL's federation gateway should make GraphQL better suited for enterprise scale adoption.
Vasudev Lal on LinkedIn: Taming Broad/Shallow AI with Explicit Knowledge & Bridging Human-AI
My keynote at CIKM 2021 workshop on Knowledge Injection in Neural Networks--about uses/misuses of #LLMs, importance of explicit knowledge, and our ongoing...
On Designing and Building Enterprise Knowledge Graphs. Interview with Ora Lassila and Juan Sequeda
Benedek Rozemberczki on LinkedIn: #drugdiscovery #pharmaceuticalindustry #machinelearning
The AstraZeneca Biological Insights Knowledge Graph (BIKG) system design paper from our team is out on BioArxiv. 🥳🥳 The draft: https://lnkd.in/dQKddMs8...
How do Event Graphs help analyzing Event Data over Multiple Entities?
Classical event logs have the fundamental shortcoming of describing process behavior only in isolated process executions from the viewpoint of a single case entity. Most real-life processes involve…
Jure Leskovec on Twitter
Excited to share our collaboration with @GoogleAI: SMORE is a scalable knowledge graph completion and multi-hop reasoning system that scales to hundreds of millions of entities and relations. @ren_hongyu, @hanjundai, et al.https://t.co/O2xM9iYMjihttps://t.co/BImVmbZAFi pic.twitter.com/H52aFOejkv— Jure Leskovec (@jure) November 1, 2021
SpikeX: spaCy Pipes for Knowledge Extraction
SpikeX: spaCy Pipes for Knowledge Extraction 🧶 WikiPageX links Wikipedia pages to chunks in text 💎 ClusterX picks noun chunks in a text and clusters... 13 comments on LinkedIn
Taxonomies for Data
Taxonomies are useful for managing and analyzing data, and not just content.
Daniele Grattarola on LinkedIn: #NeurIPS2021 #neuralnetworks #artificialintelligence
Let me tell you about ✨graph cellular automata✨ and why I am so excited about them: 1. Decentralized / emergent computation on graphs is a fundamental...
Michael Galkin on LinkedIn: International Semantic Web Conference on Twitter
Our work on GNNs for inductive link prediction got the best paper award at International Semantic Web Conference 2021! Wouldn't be possible without the...
Gadi Singer on LinkedIn: #ArtificialIntelligence #DeepLearning #NeuralNetworks
Do you want to know what’s in store for the future of AI? Catch these deep learning experts in a panel led by our own Gadi Singer. Panelists Gary Marcus...
Construct a biomedical knowledge graph with NLP
Learn how to combine OCR, named entity linking, relation extraction and external enrichment databases to construct a biomedical knowledge…
Anas Ait Aomar on LinkedIn: #NLP #opensource #hr | 10 comments
🎉 We are excited to announce the first release of SkillNer today (check the demo: bit.ly/3pA8CRg). Skillner is the first open-source library to extract... 10 comments on LinkedIn
DeepMind's AlphaFold 2 reveal: Convolutions are out, attention is in | ZDNet
We know a lot more about how AlphaFold 2 works, but the mystery of why proteins fold the way they do remains something of a mystery.