A new course is available at the Open Risk Academy (openly accessible) in the Data Science category: PYT26020, Overview of Python Semantic Web Tools

The course is for those interested in the overlap of Python with Semantic Web / Knowledge Graph technologies. It is a short CrashCourse format that covers a number of core frameworks / libraries such as rdflib / json-ld, owlready and pyshacl. A working installation / basic python knowledge is required. Semantic Data knowledge is not required (but is not provided beyond the bare essentials either)

As a realistic data set for illustrations and exercises we use a collection of information about European Credit Rating Agencies that is compiled into an OWL ontology.

Background domain knowledge about the Credit Ratings domain is available at the Open Risk Manual but is not really required for the course.

The data and code used in the course are available in our Github Repo

Enjoy, and as always we value your feedback on how to improve the Academy!

At Open Risk we celebrate all things open source and FOSDEM is the biggest celebration of them all!

If you are not familiar, FOSDEM is a non-commercial, volunteer-organized event centered on free and open-source software development. FOSDEM is aimed at developers and anyone interested in the free and open-source software movement. FOSDEM is held annually since 2001, usually during the first weekend of February, at the Université Libre de Bruxelles Solbosch campus in the southeast of Brussels, Belgium. Obviously this year the entire event is virtual and thus anybody anywhere in the world has equal access!.

Continuing with a tradition we started last year we will attempt to get a glimpse at what the open source community is up to those days (or at least what they want to talk about!). To get an overview of what topics are “cross-cutting” (affecting multiple projects) at FOSDEM we will use the talk abstracts (710 talk descriptions) and process it to extract unique keywords that appear across talks. You can read further details about the analysis and results here.

Enjoy!

Dear Academy users, its been a year for the history books, a year most of us would probably want to wish away. But it has also been a year of reflection, of lessons re-learned, of finding hope, purpose and community. I wish you all for 2021 health, personal and professional growth, deeper and more meaningful connections with each other. I hope that the resources and tools we create at Open Risk will be a positive contribution to your journey! Best, Philippos

People frequently use the term 'astronomical' to describe global debt levels, but is this factually true? In this commentary we discuss whether global debt can be compared with true astronomical objects! In the process we touch various issues around the design and function of monetary systems and the behavioral implications of such designs. Enjoy!

What do risk managers do for a living? A new semantic web ontology and a collection of articles at the Open Risk Manual aim to put some machine readable structure around the question!

• Blog Post
• Live Ontology
• Job Description Catalog

Enjoy!

Monte Carlo simulation can be used when the model we want to understand is too complex to yield explicit analytic models. Complexity in a system may arise from many underlying architectural characteristics. Through the use of random numbers simulation aims to address the curse of combinatorial explosion that plagues the analysis of complex systems. In this new blog post we go over a simple simulation exercise where we tackle the challenge that with 51 states and a specific allocation of the 538 electoral votes there are many permutations ("paths to victory") that lead of the desired grand total of 270 or more votes. Read more about how this simulation was setup (spoiler alert - there is nothing politically relevant here!) what we might expect to see on the basis of the law of large numbers and the central limit theoreme and watch an animated video of how the simulation statistics starts accumulating! Enjoy