#Neo4j #GraphDB has added sharding @adamcowley shows when and how to use it #softwareengineering #data #tech #opensource #tutorial [LINK]https://adamcowley.co.uk/neo4j/sharding-neo4j-4.0/ [LINK]https://neo4j.com/docs/operations-manual/4.0-preview/images/fabric-single-instance.png

.@CamSemantics announces Anzo #GraphDB v.2: RDF*, Custom SDK, Free Edition. "Imagine being able to do labeled properties, just like you do in Neo4j & other property graphs, but also have capability of RDF to help w ontologies & inferencing" #data #tech

Creating Graphs in Python using Networkx #dataviz #tutorial #datascience #data #tech

#Semantic #technology, decision intelligence, knowledge #datascience will be our companions in the next years, so it's recommended to start exploring #graphdatabases, #ontologies, knowledge representation systems #knowledgegraph #AI #2020NewYear #trends

Don’t ask what #GraphQL can do for the #SemanticWeb; ask what the SW can do for GraphQL, says @szymonklarman Perspective dominating analyses so far has been considering GraphQL an interface for #linkeddata. That's valid, but there's another way #research

"We might conclude that we don’t need to represent knowledge in graphs explicitly anymore, but one of the hottest fields right now involves using deep learning to learn embedding representations of the base knowledge graph." h/t @aaranged @gm_spacagna [LINK]http://datasciencevademecum.com/2019/12/19/knowledge-graphs-and-causality/[\LINK] [IMAGE]https://datasciencevademecum.com/wp-content/uploads/2019/12/dark_1x01_-_string_wall_full-825x510.jpg[\IMAGE]

.@TigerGraph #graphDB updates its #Cloud offering with configuration for distributed graphs, replica instances for high availability, EFS for backup/restore. Updates available by end of 2019 on #AWS, #Azure to follow in Q1 2020

Graphs are common #data structures to represent #connecteddata. To use graphs with #deeplearning, we use graph embeddings, a low dimension representations which helps generalize input data. Node2Vec aims to preserve network neighborhoods #datascience #AI

Updates on #knowledgegraphs affect services built on top of them. But not all changes are the same: some updates drastically change the result of operations based on knowledge graph content; others do not lead to any variation #research #award #iswc_conf

Managing multiple truths in #datamodeling, #ontologies & #knowledgegraphs by @palexop #semantics #vagueness #datascience #dataengineering #EmergingTech #dmzone #presentation

Reasoning over paths in large scale #knowledgegraphs has many applications. @healx #research on approach to automatically build & rank paths between source & target entity pair with learned embeddings using a knowledge base completion model #EmergingTech

Graphs #Analytics for Fraud Detection, using Graph #NeuralNetworks for Anomaly detection. GraphSAGE is Stanford #opensource project: deep neural network-based NRL toolkit, implemented in Tensorflow, making it ideal to develop an anomaly detection system

Property graphs provide a lot of flexibility in data modeling; but how do you know when to use which feature?

MP voting records are public record in the UK, detailed #data is available about how each MP has voted in each bill @PublicWhip collect data & make it accessible @joshua_e_k explores how to use a #GraphDatabase to look at MP’s voting record similarity

.@jQAssistant is a #QA tool which allows definition & validation of project specific rules on a structural level. Built upon #Neo4j #graphdatabase, can be plugged into build process. Now w/ #PlantUML class diagrams #dataviz #softwareengineering

Graph databases are shaped the way we think, so why can't people get their heads around them?

GPU->Rapids->Cugraph->Scalable Graph Analytics

After more than a decade, the supply-driven approach to publishing public (open) data has resulted in an ever-growing number of data silos. Hundreds of thousands of datasets have been catalogued and can be accessed at data portals at different administrative levels. However, usually, users do not think in terms of datasets when they search for information. Instead, they are interested in information that is most likely scattered across several datasets. In the world of proprietary in-company data, organizations invest heavily in connecting data in knowledge graphs and/or store data in data ...

When used for KR, we can treat the terms 'knowledge graph' and 'ontology' as interchangeable. But ontologies also have a broader use as specifications for dynamic, ontology-driven applications, a distinction this article emphasizes.

A powerful combination for the semi-automatic generation of insights

Visualize Kafka Streams with Neo4j by taking any data, turning it into a graph, leveraging graph processing, and piping the results back to Apache Kafka, adding visualizations to your event streaming applications.

Your one-stop introduction to the world of graph tech.

Read latest news about Reltio Connected Customer 360. Connected customer data at the heart of customer experience.

How Google may build and improve upon the Google knowledge Graph using knowledge graph reconciliation to remove ambiguity and add new entities

Slides for my summer school talk on ML and Embeddings for Knowledge Graphs

In this review, we provide a summary of recent progress in ontology mapping (OM) at a crucial time when biomedical research is under a deluge of an in…

.@DDIAlliance releases XKOS v1.2 (Extended Knowledge Organization System) specification →facilitates sharing & management of statistical classifications.(For building #Semantic Taxonomies to tag Datasets & #DataScience outputs)https://t.co/HZD1nl5Nu0#BigData #Ontologies #RDF pic.twitter.com/DDYbHqCvm7— Kirk Borne (@KirkDBorne) June 21, 2019

When building graph data models we frequently have to deal with a degree of polymorphism for our entities just like the real world. For instance – I’m a person, but I’m also a parent, a spouse, a sibling, a child, a… Implicit categorisation Sometimes the entity categories are entirely defined by relationships to other entities. […]

Read the first installment of this blog series on artificial intelligence on the ways graph technology adds necessary context for powerful AI solutions.

Increasingly complex products undoubtedly require greater management of components, function and data. Classic product data management (PDM) has long reach its limits in this respect. Breaking down product DNA is now driven by artificial intelligence (AI) and knowledge graphs. In… Read more →