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Deep learning models contributed to reaching unprecedented results in prediction and classification tasks of Artificial Intelligence (AI) systems. However, alongside this notable progress, they do not provide human-understandable insights on how a specific result was achieved. In contexts where the impact of AI on human life is relevant (e.g., recruitment tools, medical diagnoses, etc.), explainability is not only a desirable property, but it is -or, in some cases, it will be soon-a legal requirement. Most of the available approaches to implement eXplainable Artificial Intelligence (XAI) fo...

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 paper attempts to compare and combine different approaches for

existing queries remain valid) even in the face of complex additio

here's what's new in 20c: blogs.oracle.com/oraclespatial/… #oraclegraph #Analytics #DataScience https://t.co/hXlgPgxcxJ

https://t.co/4Ohse28toR

To help promoting the usage of @wikidata , here's another attempt to explain properties of statements with one of the most used qualifiers "start time" (pq:P850).Try it: https://t.co/tvOeWK7qBM#SPARQL #LinkedData pic.twitter.com/RshdPRx92D— Ivo Velitchkov (@kvistgaard) February 20, 2020

step list of how to build a KG assembled by @TheodoraPetkova w/ the help of our #semantictechnology experts. hubs.ly/H0n5B970 #semantics #SPARQL #datamodelling #linkeddata

Very nice article highlighting recent proof that complete graphs can be decomposed into smaller multiple trees. twitter.com/QuantaMagazine…

multiple tables linked by connected fields. Setting up a relational database requires a person who understands data structures. And if new information is added, or new relationships become important, the database administrator will need to change the structure of the database and, most likely, update the user interface as well. So what do you do if you have a data set where you can't map out the relationships ahead of time? Where instead of being connected by a single data point, people can be connected by things you can't predict in advance? Maybe two people are on the same baseball team or like the same types of books or live in the same city. Adding each of those items as a separa

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Building a knowledge graph the semantic data modeling way in 10 steps with the help of Ontotext's knowledge graph technology experts.

In this work, we propose Knowledge Integration Networks (referred as KINet)

At Madgex, we power job board technology for over 200 brands all around the world, and that number is growing all the time. With our scale and reach, we have a lot of data at our fingertips, so we evolved our Data Science team to look at Machine Learning and Knowledge Graph models to enhance the experience for users of our platform. We started by looking at jobs, and asking the basic question... when we talk about a ‘job’ what exactly do we mean?

world relationships to be recorded and analysed without losing information. Questions can then be asked of the data, such as the strength and direction of relationships between objects in the graph. Graphs are mathematical structures utilised to model numerous forms of relationships and processes in information

supervised learning? Given

up Lisp development style that I have used for decades using symbolic AI and knowledge representation."

The #3dscenegraph automatic semantic labeling and computation code is now available at https://t.co/08lm1L35LX! To learn more about the project visit https://t.co/vT1sLlTosC pic.twitter.com/eh4xf9DG0v— Iro Armeni (@ir0armeni) February 19, 2020

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

scale incomplete knowledge graphs

See articles in the new DATA INTELLIGENCE Journal: mitpressjournals.org/toc/dint/curre…

hand that when trying to apply graph machine learning techniques to identify fraudulent behaviour in the bitcoin blockchain data, scalability was the biggest roadblock. The bitcoin blockchain graph we are using has millions of wallets (nodes) and billions of transactions (edges) which makes most graph machine learning methods infe

based simulations in which an agent

Apply web scraping bots , computational linguistics, and natural language processing algorithms to build knowledge graphsContinue reading on Towards Data Science »

Wikipedia2Vec: #Python #opensource tool for learning word & entity embeddings from #Wikipedia. Demo: https://t.co/Gv5EBXWbuX #Research paper: https://t.co/GGbQjQolJe #datascience #AI #NLP h/t @aaranged

Facilitating #journalism #automation via #knowledgegraphs. KG nodes corresponding to news articles, arrows show their connections. Generated using @sentilecto_NLU, allows navigating the spacial representation of a set of related texts #AI h/t @aaranged

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The new language model our teams built is the largest and most powerful one ever created – a milestone with the promise to transform how technology understands and assists us. https://t.co/YvLM0HAr8u— Satya Nadella (@satyanadella) February 12, 2020