Nature Machine Intelligence - The number of graph neural network papers in this journal has grown as the field matures. We take a closer look at some of the scientific applications.

In the last couple of months, I’ve been noticing a gradual shift in the kind of articles that we receive at Data Science Central. We still get a fair amount of data science content, but increasingly (and admittedly with a bit of encouragement) we’re seeing more articles centered around graphs and semantics. I don’t believe… Read More »DSC Weekly Digest 22 February 2022: Graphology

Can Machine Learning Do Symbolic Manipulation?  I spent some time over the holidays engaged in a fascinating online conversation. The gist of it was a variation of an argument that has been going on in the realm of artificial intelligence from the time of Minsky and Seymour Papert: Whether it is possible for neural networks to… Read More »DSC Weekly Digest 04 Jan 2022: Can Machine Learning Do Symbolic Manipulation?

Papers With Code highlights trending Machine Learning research and the code to implement it.

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…

We are excited to announce that we are running an online panel this November where will explore the foundations of why graphs are the right tool for complexity...

The framework achieves great coverage and performance across different incident triaging scenarios, and also outperforms other state-of-the-art root cause analysis methodologies.

Memgraph, the streaming graph application platform, today announced Memgraph 2.0, the public launch of its source-available platform, which makes it easy

Researchers are turning to the mathematics of higher-order interactions to better model the complex connections within their data.

Ensuring the success of big graph processing for the next decade and beyond.

In my previous post in which I discussed names, I also led in with the fact that I am a writer. Significantly, I did not really talk much about that particula…

Similar Cases Recommendation using Legal Knowledge Graphs https://t.co/I9lTyMwsMF (yet another cc: for you @EmekaOkoye :) pic.twitter.com/Sqrm5uGhFF— Aaron Bradley (@aaranged) July 13, 2021

We wanted to see if the sensor data can be brought together through the Node Red low-code/no-code framework to feed the data into RDFox, an in-memory triplestore and then data summaries periodically pushed to a central, larger triplestore.

A few slides on the applications of Graph Neural Networks https://lnkd.in/gyvkHrr...

Using R to extract user data from the Yelp API and create network graphs

The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information. At its core, the concept of ontology provides the means to semantically describe and structure information, and expose it to software and human agents in a machine and human-readable form. For software agents to be realized, it is crucial to develop powerful artificial intelligence and machine-learning techniques, able to extract knowledge from information sources, and represent it in the underlying ontology. This survey aims to provide insight into key aspects of ontology-based knowledge extraction from various sources such as text, databases, and human expertise, realized in the realm of feature selection. First, common classification and feature selection algorithms are presented. Then, selected approaches, which utilize ontologies to represent features and perform feature selection and classification, are described. The selective and representative approaches span diverse application domains, such as document classification, opinion mining, manufacturing, recommendation systems, urban management, information security systems, and demonstrate the feasibility and applicability of such methods. This survey, in addition to the criteria-based presentation of related works, contributes a number of open issues and challenges related to this still active research topic.

A collection of important graph embedding, classification and representation learning papers with implementations. - benedekrozemberczki/awesome-graph-classification

Graph Theory in Machine Learning, and How it’s Changed the Game

Stanford / Winter 2021

"Relational world knowledge representation in contextual language models: A review" Knowledge bases such as #Wikidata provide a high standard of factual precision which can in turn be expressively modeled by language models.(Safavi and Koutra, 2021)https://t.co/zZuhjIvCva pic.twitter.com/NCLBQBlLPL— WikiResearch (@WikiResearch) April 23, 2021

Network Analysis, Topic Modeling, and a Wordcloud!

13 months ago we released a JavaScript based SHACL implementation for validating RDF. Today I'm happy to announce our version of the SHACL playground, implemented by our @zazukocom colleague @tpluscode 🚀100% client side in your browser, try it out here https://t.co/N7cWtG1LW0 pic.twitter.com/OuJUKkJfjG— Adrian Gschwend (@linkedktk) May 1, 2021

How simple structured data trumps clever machine learning

On the role of knowledge that is structured, explicit and intelligible in providing a path toward higher machine intelligence

DIG: Dive into Graphs A research-oriented library that includes unified and extensible implementations of algorithms for (1) graph generation, (2) self-supervised...

"The UI allows individuals with no previous knowledge of the Semantic Web to query the DBpedia knowledge base...." > Interface to Query and Visualise Definitions from a Knowledge Base @anelia12430996 & Hélène De Ribaupierre https://t.co/QGSJSEq4Ab pic.twitter.com/EIhZRVikK0— Aaron Bradley (@aaranged) March 15, 2021

NetVec: A Scalable Hypergraph Embedding System https://t.co/CHaT76MA7T pic.twitter.com/ZVChTtb0zH— Aaron Bradley (@aaranged) March 18, 2021

Accurate cardinality estimates are a key ingredient to achieve optimal query plans. For RDF engines, specifically under common knowledge graph processing workloads, the lack of schema, correlated...

Basic Introduction to what Graph Analytics is…