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Photo by AndreyPopov on iStockWe all use the Google Knowledge Graph tens of times a day, but maybe not many of us are aware to be actually querying the Graph while making a simple search on Google.When you search for something, for example, “Goldman Sachs”, what you get is a list of snippets of web pages plus an Infobox next to the search results.The Knowledge Graph behind your Google search allows to enhance the search engine with specific and possibly useful features on the “entity” you are looking for (in this case Goldman Sachs), gathered from a variety of sources. So, allegedly, Google Knowledge Graph enhances the result of our search with semantics [4].Let’s now try to consider reasoning.Now, say we are studying Goldman Sachs for some reason and we wish to know whether there is some person x in Goldman Sachs board who is the CEO of some other company y that is in the Tech field?Or in other terms, in a ‘fancy’ logic conjunctive query fashion:∃ x y board(Goldm

Knowledge graphs and their relation to taxonomies, ontologies, and other knowledge organization systems.

WHAT THIS TALK IS ABOUT My personal take on the history of human label collection ter to you

Cases of the Wuhan coronavirus have been confirmed around the globe, reminding us that the capacity to quickly develop, test and deploy new drugs is essential to save lives. Unfortunately, experts say we still are years away from a vaccine for the virus. However, a type of artificial intelligence can speed up the drug development process that already exists, but it hasn’t yet been deployed at scale.

time.Therefore, we have recognised that building a “Talent Knowledge Graph” will allow us to effectively operate in our domain as well as to fully unlock the predictive power of artificial intelligence and provide unique insights from our heterogeneous data sources.After Google’s reveal of its Knowledge Graph platform in 2012, the term has been rapidly growing in popularit

I realized that I needed to know what the constituent parts of data models really are. Across the board, all platforms, all models etc. Is there anything similar to atoms and the (chemical) bonds that enables the formation of molecules?

What’s the difference between AnzoGraph and many other graph databases on the market? This infographic highlights the many nuances between them.

The best (and new) survey on the theoretical aspects of GNNs I'm aware of. So many illustrative examples of what GNN can and cannot distinguish.

OpenAI believes that huge computing power is key driver

If information does not exist in electronic form by now in your organization, it is very likely already lost.

#knowledgegraphs need #DLTs to secure keys, DLTs need knowledge graphs to provide context & provenance. Ultimately, knowledge graphs will end up being the integration point for a number of #technologies lumped under #AI #data #EmergingTech @kurt_cagle

“You must unlearn what you have learned” (Master Yoda) The “conceptual” level

.@shivamshaz #datascientist who rules #DataScience for Good competitions on #Kaggle wants to apply expertise in #ML to solve micro-finance problems for underbanked population in developing countries using network science, graph theory, unstructured data

The emerging landscape for distributed knowledge, #ontology, #semanticweb, knowledge base, graph based technologies and standards. Current trends related to graph based #technology #knowledgegeraph #analytics #graphDB #AI #datascience #longread @nfigay

Much (if not all) of the discussion about temporal issues in the last 30+ years have been based on the assumption of the necessity of SQL tables. The narrative for how to build “well-formed” SQL data models is the well-known “Normalization” procedure. Data modelers with my hair color will remember the poster, which was a give-away with the Database Programming and Design Magazine (Miller Freeman) in 1989. The title of the poster is “5 Rules of Data Normalization”. Here is a miniature image of it

Meetup organised by Connected Data London, 29th January 2019, London. Don't miss the first meet up of the year!

Data, AI, intelligent applications. They’re no longer separate topics, or even separate conferences. O’Reilly announced that it is merging its data and AI conferences, saying, “Data feeds AI; AI makes sense of data.” Further, both power applications. In 2016, Ben Lorica – then program chair of the O’Reilly Strata Data and AI Conferences – predicted that soon “some features of AI will be incorporated into every application that we touch, and we won’t be able to do anything without touching an application.”

This post is a series of 3 articles about the GraphTech ecosystem, as of 2019. This article is the first part and covers the graph database landscape.

This post presents the ecosystem of graph visualization. Read on to find the full list of graph visualization software and graph visualization libraries.

This post is a series of 3 articles about the GraphTech ecosystem. This article covers the graph analytics landscape: frameworks, libraries, query languages

The Great(er) Bear - using @Wikidata to generate better artwork. Wikipedia holds structured #data about people and things. It uses SPARQL to query that data. @edent writes something to query Wikidata to generate a more accurate artwork #dataviz #graphDB

Lee — the computer scientist best known as the inventor of the World Wide Web — himself who dreamt of a place full of readable data, neatly linked. Years later, we are working towards that goal, thanks to a vocabulary called Schema. This article tells you a bit more about how we got here.

an ontology representating the knowledge of the aviation sector

Here at Neo4j, we have a motto: Graphs Are Everywhere. This blog series was inspired by all the times I encountered graphs and “graph problems” in my non-working life. Hopefully these posts help you see more graphs in the world.… Read more →

designed in at the start. Big companies have thousands of them. In the 1990s, co

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tier_architectureHowever, knowledge graphs propose a paradigm shift to this design blurring the barrier between logic and data. By bringing some of the knowledge of the domain into a graph through rules a knowledge graph captures more than just the data in the system. As a result, rules can make the queries and requests much simpler to write and manage which in turns allows applications to be more flexible, less error prone and faster.This article will introduce a simple example to showcase the impact of rules on query design. The example will be ill