When we used to think about data, it was most often in regard to where data was going to be stored and how we would manage it. Yes, files worked for a while, but when manipulating data became an important business priority across industries, the “file” solution didn’t work so well anymore. To meet these increasing demands, applications were designed and developed, addressing data storage and manipulation needs simultaneously — thus, the “database” was born. Today, data is viewed quite differently. Beyond data manipulation, organizations are focusing on mining their data for more visibility into and a deeper understanding of the intelligence within that data. Utilizing the insights acquired from their data to help make informed business decisions is a key priority for business leaders and a major concern in the development, evolution, and adoption of database solutions. A new term emerged in the industry — digital assets. That data the world has been obsessing over.

Lee — to pioneer ways of giving people control over their own personal data. Solid maintains that people should be free to share “whatever they want with whomever they want.” The key to this mechanism of control is a personal data “Pod”

life connections will make the technology particularly useful for investment management in 2020 and the years to com

This post commemorates the first anniversary of the series where we examine advancements in NLP and Graph ML powered by knowledge graphs! 🎂 1️⃣

suited tool to present data where connections and links are important for us to understand it. Like molecules structure that presents a collection of basic atoms which are linked to other, forming complex structure where each atom’s connection in this collection means something’s in terms of the usage or the characteris

Technology research and analyst house Bloor Research places Ontotext as champion among RDF graph providers in its latest update on the graph database market

Discover what's new in the Neo4j community for the week of 18 July 2020, including Graph Embeddings and executives of Belgian companies.

In 2012 I started my .NET implementation of R2RML and RDB to RDF Direct mapping which I called r2rml4net. It never reached the maturity it should have but now, 8 years later, I have little choice but to polish it and use it for converting my database to triples. A task I had originally intended but never really completed. Why is it significant? Because all those years later the environment around R2RML as a standard is almost as broken, incomplete and sad as it was when I started. Let’s explore that as an example of what is wrong with RDF in general. It has been brought to my attention that Morph is in fact actiavely maintained. I’ve updated it’s details and evaluation. Intro. What is R2RML? R2RML and Direct Mapping are two complementary W3C recommendation (specifications) which define language and algorithm respectively which are used to transform relation databases into RDF graphs. The first is a full blown, but not overly complicated RDF vocabulary which lets designers

Welcome to this week’s #GraphCast – our series featuring what you might have missed in Neo4j media from the past fortnight.

Presentation Summary

learning, understanding what an entity is will help you connect content and search engines better understand your website.

Money laundering is among the hardest activities to detect in the world of financial crime. Funds move in plain sight through standard financial instruments, transactions, intermediaries, legal entities and institutions – avoiding detection by banks and law enforcement. The costs in regulatory fines and damaged reputation for financial institutions are all too real. Neo4j provides an advanced, extensible foundation for fighting money laundering, reducing compliance costs and protecting brand value.

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Graph Databases are a great solution for many modern use cases: Fraud Detection, Knowledge Graphs, Asset Management, Recommendation Engines, IoT, Permission Management … you name it.  All such projects benefit from a database technology capable of analyzing highly connected data points and their relations fast – Graph databases are designed for these tasks. But the nature of graph data poses challenges when it comes to *buzzword alert* scalability. So why is this, and are graph databases capable of scaling? Let’s see... In the following, we will define what we mean by scaling, take a closer look at two challenges potentially hindering scaling with graph databases, and discuss solutions currently available. What Is the “Scalability of Graph Databases”? Let’s quickly define what we mean here by scaling, as it is not “just” putting more data on one machine or throwing it on various ones. What you want when working with large or growing datasets is also an acceptabl

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consuming because experts are required to pick out biomedical inte

What is more important than data consolidation? Making sense of that data and communicating it to others. With data volumes growing and time for analysis decreasing, this doesn't seem like an easy task. Data visualisation, however, may be the most reliable way to get on with this daunting process and make it seamless. Data visualisation and more specifically graph visualisation helps you organize all your data and make it readable and insightful.

quality knowledge and expertise. Modern organizations expose their knowledge with conversational interfaces such as bots and expert systems so customers, partners, and employees will have immediate access to the knowledge that drives success. We, data scientists and engineers, are responsible to make that happens. We need to answer a simple question: How do you represent business knowledge so it is easy and simple to consume? There are many approaches and possible strategies for exposing knowledge. in this article I want to dig into the good old Frequently Asked Questions system and discuss how to implement it with the latest AI technologies.In the prehistoric era, websites used to have this one FAQ page with a long and tedious list of useless questions. Only real optimists would ever search this list to find a possible remedy for an issue they face. Those years are gone. Today modern web sites have an integrated bot that user

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

driven social media company, it is essential to pursue a basic CRUD (Create, Read, Update, Delete) operation to add more people, add new messages to the current database or find associated information. This helps the company figure out a certain user’s profile. For example, it might find that user Jeff has many posts relating to AI and music. Thus we may conclude him being a programmer, and so on. Sometimes requirements for one analysis can be extremely tricky. For example, you might have a query for Find all posts from users and count the number of posts up to 3 levels down [1].

An Introduction to Graph Theory by @mpvenables Before diving in, we need to understand #data structure & networks in #machinelearning. Networks are useful for #apps, from driving directions to social networks #datascience #tutorial #analytics #AI [LINK]https://towardsdatascience.com/an-introduction-to-graph-theory-24b41746fabe[/LINK] [IMAGE]https://miro.medium.com/max/480/1*rnZ3FbSvWMVvcRP78fXeCg.png[/IMAGE]

#data stored in graphs mimic the way humans understand information. @ontotext built a Star Wars #Knowledgegraph using RDF. But…what do #software developers want? Many APIs are built using #GraphQL #JSON. “A query language for your API” #GraphDB #database

A new formal framework based on category theory which provides formal definitions of the main basic features of RDF and SPARQL. Proposal to define notions of RDF graphs as well as SPARQL basic graph patterns as objects of some nested categories #research http://arxiv.org/abs/1910.07519

#Amazon Neptune #graphDB now supports @apachetinkerpop 3.4.1. @gfxman shows examples of new features in the Gremlin query/traversal language #softwaredevelopment #analytics #database #data #tech #tutorial #opensource #AWS [LINK]https://muawia.com/amazon-neptune-now-supports-tinkerpop-3-4-features/[/LINK] [IMAGE]https://s.put.re/NzhUFENd.png[/IMAGE]

.@TDataScience talks about inference on #SemanticWeb and how to apply in a local #graphDB. What is Inference? What is it used for? Types of the procedure, Graph #Database & #Ontology, Inference in a Database #knowledgegraph #semantics #tutorial

#knowledgegraphs differ from relational DBs primarily in how information gets stored. KGs consist of index holding at least three values: subject, predicate, object (triple). In typical triple stores, the index is the database. This has several advantages

#KnowledgeGraphs and Knowledge Networks: The Story in Brief published in #IEEE Internet Computing #research #datascience #analytics #AI @amit_p h/t @WikiResearch @cyberandy

#KnowledgeGraphs and Knowledge Networks: The Story in Brief #research #datascience #analytics #AI @amit_p h/t @WikiResearch @cyberandy