GraphNews

We’ve been asked to comment on the applicability of Knowledge Graphs and Semantic Technology in service of a couple of common use cases. We will draw on our own experience with client projects as well as some examples we have come to from networking with our peers.

And how Graph Theory can prove itContinue reading on Towards Data Science »

The Linked Data ecosystem and RDF as its graph data model have been around for many years already. Even though there is an increasing interest in knowledge graphs, many developers...

A knowledge scientist is a person who builds bridges between #business requirements, questions, and #data. The goal is to document knowledge @juansequeda #AI #machinelearning #businessintelligence #analytics https://www.infoworld.com/article/3448577/who-should-be-responsible-for-your-data-the-knowledge-scientist.html

According to Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

As most of the global workforce goes remote in 2020, businesses and budgets are changing due to a decrease or increase in site traffic. Since this pandemic is new for marketers and organizations, there isn’t exactly a marketing blueprint but one channel is proven to outlast pandemics: Search Engine Optimization (SEO).

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

19 pandemic is impacting data and analytics strategies in profound ways. Experts explain what your company should be doing right now.

Why graph databases are becoming mainstream and their connection to AI, ML and big data use cases.

This post made it to #3 on HackerNews front page. Do engage in discussion there and show us love by giving us a GitHub star.

Graph databases are becoming more important to analytics by offering a capability to store relationships and perform unique algorithms.

This is a continuation of the discussion that originates on this StackExchange post, about recommender systems using knowledge graphs(KGs). For those who might not prefer reading the original post,...

Why is it so hard to standardize a Graph Query Language ? It is because graph databases are strongly dependent on the data model and the physical layer... 37 comments on LinkedIn

A knowledge base can be thought of as a data encyclopedia that’s specific to an organization, subject domain or location.

A critical distinction between machines and humans is the way in which we reason about the world: humans through high order semantic abstractions and machines through blind adherence to statistics.

In 2012 I started my .NET implementation of R2RML and RDB to RDF Direct mapping which I called r2rml4net. It never reached the maturity it should have but now, 8 years later, I have little choice but to polish it and use it for converting my database to triples. A task I had originally intended but never really completed. Why is it significant? Because all those years later the environment around R2RML as a standard is almost as broken, incomplete and sad as it was when I started. Let’s explore that as an example of what is wrong with RDF in general. It has been brought to my attention that Morph is in fact actiavely maintained. I’ve updated it’s details and evaluation. Intro. What is R2RML? R2RML and Direct Mapping are two complementary W3C recommendation (specifications) which define language and algorithm respectively which are used to transform relation databases into RDF graphs. The first is a full blown, but not overly complicated RDF vocabulary which lets designers

I’m a big fan of Schema.org. A while back I generated an OpenAPI 2.0 (fka Swagger) definition for each one and published to GitHub. I’m currently cleaning up the project, publishing them as OpenAPI 3.0 files, and relaunching the site around it. As I was doing this work, I found myself thinking more about why Schema.org isn’t the goto schema solution for all API providers. It is a challenge that is multi-layered like an onion, and probably just as stinky, and will no doubt leave you cryin

AnzoGraph is now available on Kubernetes. By deploying in this environment, you can easily define and install our graph database in cluster on-demand.

Organizations moving to graph-based intelligence need to look at the core commercial, operational, logistical questions they want to answer first, then build graph #datamodel to optimize for relationships in the business question #graphDB @DeniseKGosnell

A knowledge graph can understand what they’re actually looking for in the real world, and it can get your customers exactly what information they need.

Welcome to PyCon India CFP Technical talks are the most important event at PyCon India, the core of the conference essentially. Two of the four days are dedicated to talks. Talks are short lectures (30 min slot) supported by a presentation. Speakers come from the Python community. Talks are selected through a CFP (Call For Proposals) process. Interested members of the community propose their talks. An editorial panel designated by the organizers makes the selections. The 2018 edition of the conference saw some 267 proposals, of which 31 were selected. CFP applications from the previous year...

Wikidata is arguably one of Wikipedia's most successful sister projects. It had a profound impact on Wikipedia in just a few years.

.@Wikidata is a community-maintained knowledge base that epitomizes FAIR principles of Findability, Accessibility, Interoperability, Reusability. Collection of #opensource tools simplify addition synchronization of Wikidata w source #databases h/t @danbri

page paper goes into a fair bit of detail about the proposal. The basic idea of how the system w

This week I’m attending a conference titled Collaborations Workshop 2019, run by the Software Sustainability Institute of the UK. The conference focuses on interoperability, documentation, tr…

When academic institutions write software what licenses do they use? Try this #sparql query on the @wikidata Query Service: https://t.co/3jV3dlNcng #lovedataweek #lovedata19 #digipres #eaasi pic.twitter.com/wXkNIBZpQm— WikiDigi (@WikiDigi) February 14, 2019

Wikipedia graph dataset. Wikipedia viewership activity (pagecounts) (Apache Cassandra) + graph structure representing web network of Wikipedia (Neo4J).

"Extracting Novel Facts from Tables for Knowledge Graph Completion" - A new method for to extract novel facts from tables, based on a scalable graphical model using similarities of entities from #DBPedia and #Wikidata.(Kruit et al., 2019)https://t.co/JiAuJuhqw8 pic.twitter.com/ttOrzPhF1w— WikiResearch (@WikiResearch) July 10, 2019

Graph #software ability to uncover context makes #AI & #ML #apps more robust. That’s part of why between 2010-2018 #research mentioning graphs has risen 3X+: less than 1,000 -> over 3,750 @AmyHodler @computerweekly h/t @KirkDBorne #graphDB #datascience