GraphNews

Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge to adopt an integrative research approach. We are facing an urgent need to effectively integrate and assimilate complementary datasets to understand the biological system as a whole. The Semantic Web offers technologies for the integration of heterogeneous data and their transformation into explicit knowledge thanks to ontologies. We have devel...

#Knowledgegraphs are a natural fit for knowledge management: they model domains to retain more context & meaning even as information is parsed and abstracted for digital representation. Information is modeled in a way that is more intuitive & useful

Read the fifth and final installment of our blog series on artificial intelligence and the ways AI explainability adds context for credibility.

Read the first installment of this blog series on artificial intelligence on the ways graph technology adds necessary context for powerful AI solutions.

Karuna Pande Joshi, University of Maryland, Baltimore County You’re trailing bits of personal data – such as credit card numbers,

Deep learning is transforming the field of artificial intelligence, yet it is lacking solid theoretical underpinnings. This state of affair significantly hinders further progress, as exemplified by time-consuming hyperparameters optimization, or the extraordinary difficulties encountered in adversarial machine learning. Our three-day workshop stems on what we identify as the current main bottleneck: understanding the geometrical […]

Causal reasoning is a necessary ingredient to human-level artificial intelligence. We're not there, yet.

Knowledge Representation is a key focus for most modern AI texts. Many AI experts feel that over half of their work is understanding how to find the right know…

The current artificial intelligence image recognition tools used in DAM systems are comparable to a pair of eyes disconnected from the brain.

Read about LinkedDataHub, how it developed as a Knowledge Graph management system and what you can use it for

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Alan Morrison's answer: Graph databases are often used to store knowledge graph data and the accompanying description, predicate and rule-based logic. Knowledge graph: A knowledge graph is a knowledge base that’s made machine readable with the help of logically consistent, linked graphs that tog...

What are your options for visualizing a network with many attributes? We review the alternatives and introduce a typology in a new state of the art report. #eurovis #datavis https://t.co/96dhomPTRt w. @carolinanobre84 @miriah_meyer @marc_streit 1/8 pic.twitter.com/PCAk5ptBp3— Alexander Lex (@alexander_lex) June 4, 2019

#knowledgegraph-based #research to design a 20 questions game on Bollywood movies. Uses probabilistic learning model for template-based question generation & answer prediction. Dataset of interrelated entities represented as weighted knowledge graph #AI

#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]

Amazon Neptune now supports the Apache TinkerPop 3.4.1 release. In this post, you will find examples of new features in the Gremlin query and traversal language such as text predicates, changes to valueMap, nested repeat steps, named repeat steps, non-numerical comparisons, and changes to the order step. It is worth pointing out that TinkerPop 3.4 […]

#graphDB #Amazon Neptune now supports full-text search integration with Elasticsearch clusters. Using Elasticsearch users can run full-text search query types such as match query, intervals query, query strings using extensions to Gremlin & SPARQL #AWS

The latest Amazon Neptune release brings together a host of capabilities that enhance developer productivity with graphs. This post summarizes the key features we have rolled out and pointers for more details. Getting started This new engine release will not be automatically applied to your existing cluster. You can choose to upgrade an existing cluster […]

#Amazon Neptune #graphDB now offers a workbench, an in-console experience to query your graph. It lets users query Neptune w #Jupyter #notebooks using Gremlin or SPARQL #datascience #cloud #knowledgegreaph #AI #database AWSreinvent2019

enterprise context, the problem arises of managing information sources that do not use the same technology, do not have the same data representation, or that have not been designed according to the same approach. Thus, in general, gathering information is a hard task, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure are unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. Over the years, several data integration solutions have been proposed:

The autonomous driving (AD) industry is exploring the use of knowledge graphs