GraphNews

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 🧶 WikiPageX links Wikipedia pages to chunks in text 💎 ClusterX picks noun chunks in a text and clusters... 13 comments on LinkedIn

Taxonomies are useful for managing and analyzing data, and not just content.

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...

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...

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...

Learn how to combine OCR, named entity linking, relation extraction and external enrichment databases to construct a biomedical knowledge…

🎉 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

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.

A new class of GNNs! This Tuesday in the #graph reading group, James Rowbottom and Ben Chamberlain present their "GRAND: Graph Neural Diffusion" paper ...

Amazon.com: Knowledge Graphs and Big Data Processing (Lecture Notes in Computer Science Book 12072) eBook : Janev, Valentina, Graux, Damien, Jabeen, Hajira, Sallinger, Emanuel: Kindle Store

Joins play a pretty important role for defining the semantics of evaluating SPARQL queries even though they are not a part of the SPARQL syntax. One does not have to think about joins as long as their queries are restricted to basic graph patterns. However once more complex constructs appear in the query, their results are typically combined using the good old relational join operator. It has certain quirks, for example, in how it deals with nulls, and it's important to understand those to avoid result explosion and performance problems.

This week’s publication of the Pandora Papers--which the International Consortium of International Journalists based on a trove of private data leaked

While many of the programming libraries encapsulate the inner working details of graph and other algorithms, as a data scientist it helps a lot having a reason…

Distribution Knowledge Embedding for Graph Pooling https://t.co/YsQBXwbFtZ pic.twitter.com/ZXbpOKDrLC— Aaron Bradley (@aaranged) September 30, 2021

Super nice talk by @matej_zecevic on #Neuro-#Causality and our integration of graph neural networks and structural causal models. 🎞️👉 https://t.co/S2XNuOqZ61 🙏 to @JackccLu for inviting Matej! pic.twitter.com/jamZ20WoGt— Kristian Kersting (@kerstingAIML) September 28, 2021

Bob Kasenchak discusses the uses and abuses of polyhierarchy. It is not a license to play fast and loose with the All-Some Rule.

Breadcrumbs is a blockchain analytics platform accessible to everyone. It offers a range of tools for investigating, monitoring, tracking, and sharing r...

Open Source Tool - Cybersecurity Graph Database in Neo4j - GitHub - amberzovitis/GraphKer: Open Source Tool - Cybersecurity Graph Database in Neo4j

A graph is built from a collection of nodes and relationships. Entities such as people, locations, items, or categories of data are represented by nodes; and the association between them reflects a relationship. A versatile structure like a graph enables us to model real-world applications–computer networks, social media recommendation engines, bitcoin blockchains, and more. Basing this very structure as a template, we can bring it to life by performing C.R.U.D operations through a unique management system–a graph database.

To showcase best practices for building/training Graph Neural Nets in JAX, we put together a comprehensive example for molecular activity prediction using Flax & JraphOfficial Flax GNN example: https://t.co/vrsyYpcdhhGreat work by @BigAmeya w/ collaborators @ Brain & DeepMind https://t.co/8L0UKgQxj5 pic.twitter.com/Jzzvxt7a3F— Thomas Kipf (@thomaskipf) October 8, 2021

"SYGMA: System for Generalizable Modular Question Answering Over Knowledge Bases", tested on #DBPedia and #Wikidata + a new Temporal QA benchmarkdataset based on Wikidata.(Neelam et al, 2021)data: https://t.co/0tkY9sjA9Zpaper: https://t.co/rZDw4bW56Q pic.twitter.com/XqFwp2def2— WikiResearch (@WikiResearch) October 6, 2021

ArangoDB, the commercial open source company behind the graph database system of the same name, has raised $27.8 million.

Memgraph 2.0 is here! We are finally source available and ready to tame your streams.

Our paper titled "A Survey of #RDF Stores & #SPARQL Engines for Querying Knowledge Graphs" has been accepted to #VLDB Journal. A survey of over 120 RDF stores and #KnowledgeGraphs. https://t.co/SsFroOOBI5 @aidhog @NgongaAxel @akswgroup @DiceResearch pic.twitter.com/o4fiwG1VJq— Muhammad Saleem (@saleem_muhamad) October 3, 2021

“Take a crash course on GraphQL, a tool for building APIs that's seeing rapid adoption. And Dgraph Cloud is the complete GraphQL platform to get you going."

Program Transfer and Ontology Awareness for Semantic Parsing in KBQA [Knowledge Base Question Answering] https://t.co/4UHsmhhkRz pic.twitter.com/iTyYwse9Jf— Aaron Bradley (@aaranged) October 13, 2021