In our last MET Art Collections post we ingested and processed part of a dataset containing more than 470,000 artworks from The Metropolitan Museum of Art and created a knowledge graph using Hume, GraphAware’s insights engine.This time, we will have a look at four use cases demonstrating how to get insights from the knowledge graph. We will start with Hume Visualisations to explore tag’s context; create Hume Actions to analyse the donors, and finally, use the Graph Data Science Library to suggest similar paintings.Exploring tag’s contextWe do not need complex queries to find interesting facts in the Art knowledge graph....
The Metropolitan Museum of Art recently published a dataset of more than 470,000 works of art under the CC-zero License. Representing such a collection as a knowledge graph allows us to explore it in a unique way - seeing the artworks, their authors, donors, mediums, tags, or art movements deeply connected, being able to traverse the links between them and discover unexpected relations.The inspiration to explore this dataset spring from an exciting challenge by Neo4j, the Summer of Nodes: Week 2, make sure to check it out.To create and explore the Art knowledge graph we will use Hume insights engine....
In an increasingly complex and hyperconnected world, organizations need a level of insight, collaboration and optimization into their data that is locked away within different systems and siloed within the different teams. Information is hidden and opportunities are missed because of the lack of a single system to host, analyze, and visualize their data.Welcome to Hume.Hume is the insights engine that collects your scattered data into one full graph-powered solution for your analysts to make sense of their data. Built on top of the cutting-edge technology by Neo4j, Hume has the ability to connect your structured and unstructured data into...
Graphs are a perfect fit for IT Operations. Right from dependency management to impact analysis and capacity to outage planning, the interconnectedness of the components that make up networks and services, modelled naturally as a graph enable various teams such as support, help desk and devops to navigate potentially complex relationships.BackgroundThe size of networks has been rapidly increasing and along with it, assets such as applications, services, and devices.IT managers and operations teams have been facing challenges around quick response times and incident analysis due to the inability of traditional databases, such as relational, to process heavily hierarchical and interconnected...
The GraphAware team is excited to release the neo4j-sso third-party security extension, compatible with Neo4j Enterprise, version 4.0 and above.It allows Neo4j Enterprise users to connect with their LDAP, Okta, Google or Azure Active Directory (and many other providers) accounts in a seamless and secure way.It has many benefits from a security perspective, where the two most prominent are : repudiation : it removes most of the need for service accounts where more than one user would use the same set of credentials for connecting to Neo4j, thus tracing every action against a Neo4j server back to a single individual...
GraphAware is proud to announce the release of Hume 2.6. This new release brings some major updates and exciting new features to our customers. In particular: [New] Hume.Perspectives: a mechanism for specifying multiple subschemas of the main Knowledge Graph. It improves security and readability, allowing users to specify who can read what. [Improved] Hume.Labs 2.0: the latest version of Hume.Labs improves security, aligns it to the Hume RBAC model, simplifies management of multiple projects and enormously reduces (almost to 0) the data scientists’ effort for building language models and related skills from an annotated text or dictionary. [Improved] Hume.Viz: the...
Few years ago I met Michal Bachman and some other GraphAware heroes at GraphConnect in San Francisco. In those days I also had my job interview for the company. Surprisingly, for the first time since I started my journey in IT, I didn’t find myself explaining how I could be useful despite not having a strong background in engineering and software development. On the contrary, Michal was interested in all the things I could do because of my non-traditional curriculum, with a PhD in Cultural Anthropology, some experience in JavaScript graph visualisation, and a genuine passion for networks and graphs.Any...
If you have read our post Hume in Space: Monitoring Satellite Technology Markets with a ML-powered Knowledge Graph, you surely wonder: is there a way to extract relations among named entities without heavy investment? Investment in terms of time to label training dataset and to develop, train and deploy a machine learning model?Yes, there is! But first things first …There are many ways to approach the problem. If you are a data scientist, your first instinct is probably Deep Learning (DL). Entity relation extraction, i.e. classifying relation types between named entities such as (:Person)-[:WORKS_FOR]->(:Organization), is clearly a perfect use case...
GraphTour Europe 2020 started in Amsterdam on February 4, right after the release of Neo4j 4.0, a key milestone in the graph technology landscape. At GraphAware we are very excited about the new features included in this release because they revolutionize the way we approach some common graph challenges. Our CEO, Michal Bachman spoke about this in Amsterdam in his talk “Practical Applications of Neo4j 4.0”, and he and other GraphAware experts continue to present in each of the six cities where we sponsor GraphTour. Find out what’s the next virtual event and register for free.GraphAware and Neo4jAt GraphAware we...
Neo4j has implemented very useful algorithms in order to derive insights from your graph data.The Louvain algorithm for community detection or PageRank for centrality for finding important nodes in your graph are just some examples of them.A couple of days ago, the GDS 1.2 preview was out and is now compatible with Neo4j 4.x, here are the installation instructions :Download the GDS 1.2 library from the Neo4j Download CentreUnzip the downloaded the file and copy the .jar file into the plugins directory of your Neo4j server.Amend the neo4j.conf file in order to allow the gds.* procedures (assuming you have the...