GraphNews

Introduction to timbr and the Covid-19 SQL knowledge graph

Diving into the bowels of a global organization

An overview of how Strise tackle the Entity Linking problem.

Google introduces a personalized entity repository, similar to a user-specific knowedge graph to predict actions and suggestions for users of Google.

Interview with Giovanni Campagna, one of the Almond principal developers

Happy to announce that my @OReillyMedia book Semantic Modeling for Data is now published https://t.co/4yngwDPMrO and available in electronic and print format https://t.co/VsFc8zf2KY. Get a free sample chapter at https://t.co/DivwADNUGo #datascience #datamodeling #knowledgegraphs pic.twitter.com/9j58IF1lcZ— Panos Alexopoulos (@PAlexop) September 9, 2020

A cheat sheet for quickly analyzing and evaluating relationships in graph networks using Python, Networkx, and Gensim

Using neo4j’s power for scalable graph analytics in Python

Can We Divide It Into Supervised/Unsupervised Learning? It’s Not That Simple…

Wikidata and Wikipedia have been proven useful for reason-ing in natural language applications, like question answering or entitylinking. Yet, no existing work has studied the potential of...

In-database training avoids exporting the data from the DBMS to other machine learning platforms thus better support continuous model…

A frequent source of confusion with ontologies and more generally with any kind of information system is the Open World Assumption. This trips up novice inexperienced users, but as I will argue in …

In Deliberate Internet 27 I focus on the new wave of note-taking apps and explain why they are all sprouting now.

Don t Quarantine Your Research you keep your social distance and we provide you a social DISCOUNT use QUARANTINEDAYS Code in precise requirement and Get FLAT 1000USD OFF on all CMI reports The Knowledge Graph can be defined as the ...

RDF2vec is an embedding technique for representing knowledge graph entities in a continuous vector space. In this paper, we investigate the effect of materializing implicit A-box axioms induced by...

Discover the endless possibilities of the NSMNTX plugin on RDF data, linked data and more with Neo4j Engineering Director Jesús Barrasa.

Current action recognition systems require large amounts of training data for recognizing an action. Recent works have explored the paradigm of zero-shot and few-shot learning to learn classifiers...

In graph embedding, the connectivity information of a graph is used to represent each vertex as a point in a d-dimensional space. Unlike the original, irregular structural information, such a...

Graph neural networks (GNN) represent an emerging line of deep learning models that operate on graph structures. It is becoming more and more popular due to its high accuracy achieved in many...

spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines. The main aim is to learn a meaningful low dimensional embedding of the data. However, most...

Everything you need to know to start working with GNN

A nice, easy way to enrich your spatial data with features from Graph Theory which capture information that is hard to encode otherwise.

Knowledge graph embedding approaches represent nodes and edges of graphs as mathematical vectors. Current approaches focus on embedding complete knowledge graphs, i.e. all nodes and edges. This...