https://douroucouli.wordpress.com/2019/05/10/obographviz-0-2-2-released-now-with-visualization-of-equivalence-cliques/

Analyzing US Lobbying Data in #Neo4J #graphdatabase

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haystack discoveries that evade traditional analytic workflow.

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Neo4j’s Alicia Frame explains how life science researchers can exploit graph databases to get truly granular insight into big data to make major leaps forward in medical research.Complex data sets hold the key to advancing medical breakthroughs. These data sets tend to be voluminous and heterogeneous by nature, presenting an insurmountable challenge for traditional data analysis methods as they struggle to link patterns and outcomes. The unfortunate consequence is a slowdown in the progress of research.Anyone who works in life sciences is aware that they are working with highly connected information; the challenge is making sense of these connections. Unfortunately, many scientists are still using relational databases and spreadsheets which makes mapping important patterns and connections unintuitive and difficult, if not impossible.Graph technologyGraph technology is emerging as an enabler for researchers to trawl gargantuan amounts of unstructured data, turning it into valuab

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.

According to Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

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

There are only a handful of publicly available knowledge graphs. And among those, only a few provide data with enough breadth to in some way represent the entire internet, and with enough granulari…

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

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market,” said Kendall Clark, CEO and Founder of Stardog. “We plan to expand our successful EU operations, strengthen our work in the public sector, and to develop tools and partnerships to broaden access to knowledge graphs.” Stardog’s Enterprise Knowledge Graph platform is used by industry leaders including Morgan Stanley, NASA, Schneider Electric and Bayer. Customers use Stardog for a range of solutions including operational resilienc

Hi graph gang, In this week’s video, Jans, Niels, and Morgan show how to build a social knowledge graph based on data from collaboration tools that we use at Neo4j. Rik Van Bruggen queries the COVID-19 Contact Tracing Graph, Tomaz… Read more →

Ontotext releases GraphDB 9.2 featuring the anticipated support for RDF*/SPARQL* and improvements in the plug-ins for semantic similarity and versioning.

based Gruff Drives Infinite Data Integration, Holistic Insights and Complex Reasoning

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

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

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

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.

This article has two sections. The first describes the longstanding difficulties in integrating data for analytics and more recently, data science and AI.

19, trying to make sense of the data surrounding the virus is a Herculean task. Vast in volume and ceaselessly produced, this data emanates from domains as different as virology and economics and is produced by a multitude of people and organizations. Unsurprisingly the standards to which this data conforms are as multitudinous as its sources. It just so happens that making sense of messy data from disparate sources is one of the things at which knowledge graphs excel. Moreover, knowledge graphs make it possible to derive new knowledge from intelligently connecting information residing in those disparate data repositories. Given that the ability to better analyze data and gain new insights is of obvious use to people trying to respond to the pandemic, those working with knowledge graph technologies have started to talk about how those technologies – and their skills – might

Big data combined with advances in data science approaches have provided a valuable opportunity for the pharma industry to uncover…

GraphDB has generated a thriving community over the years and they share their insights and experience in many blog posts and tutorials.

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

based diagnosis of disease, and drug repurposing. Integrating data and knowledge is a formidable challenge in biomedical research. Although new scientific findings are being discovered at a rapid pace, a large proportion of that knowledge is either locked in data silos (where integration is hindered by differing nomenclature, data models, and lice

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

The graph database market is very exciting, as the long list of vendors continues to grow. You may not know that there are huge differences in the origin story of the dozens of graph databases on the market today. It’s this origin story that greatly impacts the superpowers and weaknesses of the various offerings.