VLog, a new rule based reasoner on #KnowledgeGraphs, with #opensource implementation on #Github #iswc_conf #research #sfotwareengineering h/t

Learn about how AstraZeneca visualized patient journeys, answered important questions about prescriptions and diagnoses, and improved patient outcomes.

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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

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

Financial fraud is growing and it is a costly problem, estimated at 6% of the Global Domestic Product, more than $5 trillion in 2019.

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

Financial fraud is growing and it is a costly problem, estimated at 6% of the Global Domestic Product, more than $5 trillion in 2019.

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

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

Learn advanced Cypher queries for one approach to analyzing data inconsistencies directly in your flexible and forgiving Neo4j graph database.

Graph databases and analytics are getting ever more accessible and relevant

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Introducing the Neo4j Graph Data Science plugin with examples from the “Graph Algorithms: Practical Examples in Apache Spark and Neo4j” bookIn the past couple of years, the field of data science has gained much traction. It has become an essential part of business and academic research. Combined with the increasing popularity of graphs and graph databases, folks at Neo4j decided to release a Graph Data Science (GDS) plugin. It is the successor of the Graph Algorithms plugin, that is to be deprecated.Those of you who are familiar with Graph Algorithms plugin will notice that the syntax hasn’t changed much to allow for a smoother transition. To show what has changed, I have prepared the migration guides in the form of Apache Zeppelin notebooks that can be found on GitHub.Neo4j connector for Apache Zeppelin was developed by Andrea Santurbano, who also designed the beautiful home page notebook of this project and helped with his ideas. In the migrations guides, we used the ex

This article shows how an RDF Graph CRUD application can be rapidly developed, yet without losing the flexibility that HTML5/JavaScript offers, from which it can be concluded that there is no reason preventing the use of RDF Graphs as the backend for production-capable applications.

Discover a new way to visualize and explore your connected data with SemSpect: a unique approach to graph visualization that doesn't depend on using random or best-guess Cypher queries in order to explore your data's meta-graph and that is compatible with Neo4j (including RDF datasets).

Graphs are a general-purpose #datamodel, as relational was a general-purpose #data model a generation ago. A supply chain is a graph. Knowledge is a graph. Graphs are very applicable in a wide range of use cases @jimwebber @TheRegister #GraphDB #tech [LINK]https://www.theregister.co.uk/2020/02/05/graph_database_neo4j_chief_scientist/ [LINK]https://regmedia.co.uk/2016/04/26/graph_database.jpg

Representing Graph #Data Structures: Edge Lists, Adjacency Matrices, Adjacency Lists #datascience #analytics #tutorial #datamodel #softwareengineering #data #tech

#Neo4j #GraphDB has added sharding @adamcowley shows when and how to use it #softwareengineering #data #tech #opensource #tutorial [LINK]https://adamcowley.co.uk/neo4j/sharding-neo4j-4.0/ [LINK]https://neo4j.com/docs/operations-manual/4.0-preview/images/fabric-single-instance.png

.@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

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

#Oracle offers #GraphDatabase, Graph #Analytics free of charge with purchase of main Oracle product license. 2 different products: Database built over Oracle's relational store using RDF/SPARQL. Analytics in-memory engine, custom query language

.@TigerGraph #graphDB updates its #Cloud offering with configuration for distributed graphs, replica instances for high availability, EFS for backup/restore. Updates available by end of 2019 on #AWS, #Azure to follow in Q1 2020

Graphs are common #data structures to represent #connecteddata. To use graphs with #deeplearning, we use graph embeddings, a low dimension representations which helps generalize input data. Node2Vec aims to preserve network neighborhoods #datascience #AI

Managing multiple truths in #datamodeling, #ontologies & #knowledgegraphs by @palexop #semantics #vagueness #datascience #dataengineering #EmergingTech #dmzone #presentation

Graphs #Analytics for Fraud Detection, using Graph #NeuralNetworks for Anomaly detection. GraphSAGE is Stanford #opensource project: deep neural network-based NRL toolkit, implemented in Tensorflow, making it ideal to develop an anomaly detection system