By the mid 2000s, it was clear that the vision of the semantic web, as set out by Tim Berners Lee in 2001, despite a huge amount of initial hype and investment, remained largely unrealized. The basic idea — that a network of machine readable, semantically rich documents would form a ‘semantic web’ much like the world wide web of documents, was proving much more difficult than had been anticipated. This prompted the emergence of the concept of ‘linked data’, again promoted by Tim Berners Lee, and laid out in two documents published in 2006 and 2009.Linked data was, in essence, an attempt to reduce the idea of the semantic web down to a small number of simple principles, which were meant to capture the core of the semantic web without being overly prescriptive over the details.The 4 basic principles were:

based, and connectionist reasoning”

With some Python, JavaScript, and of course, SPARQL.

Rock and Roll!

Deep learning models contributed to reaching unprecedented results in prediction and classification tasks of Artificial Intelligence (AI) systems. However, alongside this notable progress, they do not provide human-understandable insights on how a specific result was achieved. In contexts where the impact of AI on human life is relevant (e.g., recruitment tools, medical diagnoses, etc.), explainability is not only a desirable property, but it is -or, in some cases, it will be soon-a legal requirement. Most of the available approaches to implement eXplainable Artificial Intelligence (XAI) fo...

This paper attempts to compare and combine different approaches for

step list of how to build a KG assembled by @TheodoraPetkova w/ the help of our #semantictechnology experts. hubs.ly/H0n5B970 #semantics #SPARQL #datamodelling #linkeddata

Building a knowledge graph the semantic data modeling way in 10 steps with the help of Ontotext's knowledge graph technology experts.

In this work, we propose Knowledge Integration Networks (referred as KINet)

At Madgex, we power job board technology for over 200 brands all around the world, and that number is growing all the time. With our scale and reach, we have a lot of data at our fingertips, so we evolved our Data Science team to look at Machine Learning and Knowledge Graph models to enhance the experience for users of our platform. We started by looking at jobs, and asking the basic question... when we talk about a ‘job’ what exactly do we mean?

scale incomplete knowledge graphs

based simulations in which an agent

Apply web scraping bots , computational linguistics, and natural language processing algorithms to build knowledge graphsContinue reading on Towards Data Science »

Wikipedia2Vec: #Python #opensource tool for learning word & entity embeddings from #Wikipedia. Demo: https://t.co/Gv5EBXWbuX #Research paper: https://t.co/GGbQjQolJe #datascience #AI #NLP h/t @aaranged

Facilitating #journalism #automation via #knowledgegraphs. KG nodes corresponding to news articles, arrows show their connections. Generated using @sentilecto_NLU, allows navigating the spacial representation of a set of related texts #AI h/t @aaranged

New from Google Research! REALM: https://t.co/kS2oTyxAAjWe pretrain an LM that sparsely attends over all of Wikipedia as extra context. We backprop through a latent retrieval step on 13M docs. Yields new SOTA results for open domain QA, breaking 40 on NaturalQuestions-Open! pic.twitter.com/DYDFX69Td8— Kelvin Guu (@kelvin_guu) February 11, 2020

This is great, and don’t miss the other ontologies at this excellent subdomain name.

Our physical embedding model for #knowledgegraphs achieve quasi-linear scalability. Check out the video by @_CaglarDemir at https://t.co/9n8tkZoWXg #MachineLearning #OpenScience @knowgraphs— Axel Ngonga (@NgongaAxel) February 14, 2020

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At linked data-driven companies you will find it difficult to distinguish between “traditional” data workers and those in other functional areas who, at other companies, are less reliant on data.

This article shows how an RDF Graph CRUD application can be rapidly developed, yet without losing the flexibility that HTML5/JavaScript offers, from which it can be concluded that there is no reason preventing the use of RDF Graphs as the backend for production-capable applications.

Overview of the research domain and works of the joint research team Wimmics (UCS, Inria, CNRS, I3S) in Sophia Antipolis, France

Making decentralized Web app development fun ◆ While the Semantic Web community was fighting its own internal battles, we failed to gain traction with the people who build apps that are actually used: front-end developers. Ironically, Semantic Web enthusiasts have failed to focus on the Web; whereas our technologies are delivering results in specialized back-end systems, the promised intelligent end-user apps are not being created…

What is an application network? And why does it need a graph? MuleSoft's founder and CTO help explain today's announcement at Dreamforce 2018

Discover a new way to visualize and explore your connected data with SemSpect: a unique approach to graph visualization that doesn't depend on using random or best-guess Cypher queries in order to explore your data's meta-graph and that is compatible with Neo4j (including RDF datasets).

Learn how to use Neo4j to integrate a BigQuery public dataset with Stackdriver logs into a graph database, to surface new conclusions from complex data.