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Google introduced to the general public the term Knowledge Graph (“Things not Strings”) when they added the information boxes that you see to the right-hand side of many searches. However, the benefits of storing information indexed around the entity and its properties and relationships are well-kno

constructed reports, that is easy, but as they become long, then things get really difficult. Thankfully there are a whole bunch of tools that are now becoming available that allow for the extraction of insights.The tools are possible owing to the advancements in natural language processing — the use of computational techniques and models to analyse natural language — language as it is used around us — in the documents, in voice, in chats. The explosion of content generated, and especially Wikipedia — has made various advancements possible. Thanks to Wikipedia, which contains topics arranged in a structured manner, and thanks to the effort put into translation by Go

Within a growing organization, finding the right information can take up a good part of your day.

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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Dr Nicolas FigayDigital Enterprises Organisation and Collaboration around Manufacturing and Product Data2w · EditedEmerging Landscape of #graphs related technologies: required move from da facto standards to #ISO open standard?

Do you want to learn more about #knowledgegraphs and data-centric architecture? @AlanMorrison put together a list of resources #datacentric #dataarchitecture #semantics #LinkedData #GraphDatabase

What does broccoli soup contain and Edamam’s live food knowledge graph of semantically interlinked data from various sources

This week eBay announced that they are making their internal distributed knowledge graph available through the an Apache 2.0 Open Source…

For #eBay, the #application/infrastructure #knowledgegraph is a heterogeneous property graph that improves architectural visibility, operational efficiency, #developer productivity. How it was developed, benefits, use cases #data #analytics #devops #AI [LINK]https://tech.ebayinc.com/research/ebays-new-approach-to-managing-a-vast-service-architecture/[/LINK] [IMAGE]https://tech.ebayinc.com/assets/Uploads/Editor/_resampled/ResizedImageWzE2MDAsNjcwXQ/image66.png[/IMAGE]

From loticoSpeaker: Jeen Broekstra

#Knowledgegraphs are dynamic, evolve over time w addition or deletion of triples. Most existing models focus on embedding static KGs. New context-aware Dynamic Knowledge Graph Embedding (DKGE) method supports embedding learning in online fashion #research

Eighteenth-Century Poetry Archive (ECPA) is a collaborative digital collection and research project devoted to the poetry of the long eighteenth century.

So yeah. Today Gartner named Graphs on their top 10 trends for data in 2019, stating that "graph DBMSs will grow at 100 percent annually through 2022." 💪💪💪 pic.twitter.com/LsCg38eyON— Emil Eifrem (@emileifrem) February 18, 2019

This article summarizes recent #research on Emotion Recognition Using Graph #NeuralNetworks. DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation #AI #deeplearning #knowledgegraph #datascience #innovation #tech #data https://towardsdatascience.com/emotion-recognition-using-graph-convolutional-networks-9f22f04b244e?source=rss----7f60cf5620c9---4

Modelling knowledge as it *is*, not as it *should be*

based, and connectionist reasoning”

We announce the first release of the knowledge graph for public procurement, integrating tender and company data. TheyBuyForYou is a three year initiative bringing together researchers, innovators and public administrations from 5 European countries. It is supported by EU H2020. The aim of the programme is to make procurement data more easily accessible and hence facilitate better decisions in areas such as economic development, demand management, competitive markets and vendor intelligence.

Introduction

In this paper we introduce the latest version (Version 2.0) of OSCAR, the OpenCitations RDF Search Application, which has several improved features and extends the query workflow comparing with the previous version (Version 1.0) that we presented at

It’s no longer necessary to choose between RDF & LPG #knowledgegraphs. RDF* offers the best of both worlds so you don’t have to give up anything. #AI #EmergingTech #data #analytics #datascience

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A knowledge graph connects all data without moving or copying it. A knowledge graph seamlessly layers atop the existing data infrastructure, and reveals the relationships within the data, regardless of its source or format. These graphs are highly scalable, and retain each analysis, which acts as a reusable asset.

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This paper attempts to compare and combine different approaches for

Alethio brings you more than 18,000,000,000 facts about Ethereum

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.

With the constant growth of Internet of Things (IoT) ecosystems, allowing them to interact transparently has become a major issue for both the research and the software development communities. In this paper we propose a novel approach that builds semantically interoperable ecosystems of IoT devices. The approach provides a SPARQL query-based mechanism to transparently discover and access IoT devices that publish heterogeneous data. The approach was evaluated in order to prove that it provides complete and correct answers without affecting the response time and that it scales linearly in la...