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bases and in addition, it includes a multitude of different international chapters/language communities.On the other hand, timbr DBpedia represents a synergy between DBpedia + SQL. Permits the querying of the DBpedia ontology/Open Knowledge Graph (OKG) via s

The interest in Freebase, Wikidata, and DBpedia since Wikidata's launch and geographically over the world (based on Google Trends).Fascinating to see the local distributions, and how slow the decline of Freebase is. pic.twitter.com/EstBNjCT2g— Denny Vrandečić (@vrandezo) February 27, 2020

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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 →

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Deep learning is everywhere right now, in your watch, in your televisor, your phone, and in someway the platform you are using to read…

Discover tips and strategies for implementing graph embedding into a Neo4j graph database, with plenty of Cypher examples.

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…

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infection, repeated use of the public places and transportation, the spreading path of the virus becomes a network structure. Thus a

19 Company News Tracker to help customers quickly and effectively identify signals of risk and opportunity in news data.

Criminals are constantly finding new and more sophisticated ways to commit fraud. Every technological development presents new opportunities for illicit activities, and few more so than the evolution of digital currencies.

In case you have little to no experience with RDF you might want to read the RDF Primer first, which gives a good basic introduction to the concepts of RDF.

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

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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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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