.@CamSemantics announces Anzo #GraphDB v.2: RDF*, Custom SDK, Free Edition. "Imagine being able to do labeled properties, just like you do in Neo4j & other property graphs, but also have capability of RDF to help w ontologies & inferencing" #data #tech

powered applications with minimum friction.Broad compatibility with existing and future software developm

grade features and scale. We appreciate your candid stories and collaboration, and we’ve used this to create a better solution. As such, we’re excited to announce Neo4j for Graph Data Science™, the first data science environment built to harness the predictive power of relationships for enterprise deployments. Neo4j for Graph Data Science is an ecosystem of tools that includes: With Neo4j for Graph Data Science, data scientists are empowered to confide

Personally, I am always most excited by technology when it can have a positive impact on society and the lives of ordinary people. Curing…

We are super excited to share the latest upgrades to ArangoDB which are now available with ArangoDB 3.5. With the fast-growing team, we could build many new and long-awaited features in the open-source edition and Enterprise Edition. Get ArangoDB 3.5 on our download page and see all changes in the Changelog. Need to know more […]

Augmented analytics tools, NLP search and graph analytics are the top trends for 2019 and beyond. Experts say augmented analytics and NLP are transforming how enterprises consume data and derive in...

We're excited to announce the public release of Akutan, a distributed knowledge graph store, under the Apache 2.0 open source license. Akutan is the result of four person-years of exploration and engineering effort, so there's a lot to unpack here! This post will discuss what Akutan is, how it's implemented, and why we've chosen to release it as open source.

MP voting records are public record in the UK, detailed #data is available about how each MP has voted in each bill @PublicWhip collect data & make it accessible @joshua_e_k explores how to use a #GraphDatabase to look at MP’s voting record similarity

2 together with its impact on human health. One aspect of this is organizing existing and emerging information about viral and host cell molecular biology, disease epidemiology, phenotypic progression, and effect of drugs and other treatments in individuals.

It seems like every crime show includes a few scenes where we see that the detective has built a wall of pictures, newspaper clippings, index cards and other interesting documents, linking all these things together through a network of string and push pins. This linked data allows them to step back and see how the facts relate and helps provide the bigger picture of what happened and how to solve it. Seeing the bigger picture allows detectives to use inductive and deductive reasoning to pursue leads,identify gaps in their knowledge and continue the investigation in ways they may not have seen before.The crime wall is a physical representation of knowledge or context. The crime wall helps investigators see the relationships and understand the true meaning of the facts surrounding a case. Understanding context can lead to accelerated insights and increase the productivity of the detectives. In the digital world, we can represent knowledge through similar techniques. We call this di

Leveraging the Neo4j Community Graph For Social Discovery

My new paper on ontologies in Data and SW engineering https://t.co/jqQAEMZbEl @nimonika— Christian Dirschl (@HaintzD) January 3, 2019

answering service — among other things.Expanding a knowledge graph often involves integrating it with another knowledge graph. But different graphs may use different terms for the same entities, which can lead to errors and inconsistencies during integration. Hence the need for automated techniques of entity alignment, or determining which elements of different graphs refer to the same entities.In a paper accepted to the Web Conference, my colleagues and I describe a new entity alignment technique that factors in information about the graph in the vicinity of the entity name. In tests involving the in

Analysing Load and Query Times across DBPedia and Yago

Creating Graphs in Python using Networkx #dataviz #tutorial #datascience #data #tech

Knowledge graphs have emerged as a compelling abstraction for organizing world's structured knowledge over the internet, capturing relationships among key entities of interest to enterprises, and a way to integrate information extracted from multiple data sources. Knowledge graphs have also started to play a central role in machine learning and natural language processing as a method to incorporate world knowledge, as a target knowledge representation for extracted knowledge, and for explaining what is being learned. This class is a graduate level research seminar featuring prominent researchers and industry practitioners working on different aspects of knowledge graphs. It will showcase how latest research in AI, database systems and HCI is coming together in integrated intelligent systems centered around knowledge graphs.The seminar will be offered over Zoom as per the planned schedule.The seminar is open to public. Remote participants may join the seminar through Zoom. To be

In this post I explain common issues with query performance in Neo4j and how you may solve them using the Query Log Analyser tool.

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 →

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

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

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

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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based search systems do not reflect the semantics of individual input words of search queries. For example, a query for the word “house” would not return records for the words “building” or “real estate”. How can such relationships be represented in a technical system? One approach is to include synonyms. Search engines like Elasticsearch provide methods to integrate synonym lists. However, a list of synonyms itself is required for configuration.

Stardog 6.2 just shipped with scalable virtual graph caching, better Kubernetes integration, support for Amazon Redshift, and many new optimizations . Read on for the details.

Solutions Review highlights the most common graph database use cases you need to know about so you can select the best software.