Linked Data

White Paper 15, Tensor Representations of ActivityPub Networks

White Paper 15, Tensor Representations of ActivityPub Networks

Reading Time: 1 min.
Open Risk White Paper 15: Tensor Representations of ActivityPub Networks In this third Open Risk White Paper on Connecting the Dots we explore representations of online communication networks that are organized according to the ActivityPub protocol. We discuss the main relevant features of the protocol and the broader application ecosystem around it that shapes emerging online network topologies. We develop a stylized description of ActivityPub compliant networks as a mathematical multilayer network.
Connecting the Dots, Tensor Representations of Activitypub Networks

Connecting the Dots, Tensor Representations of Activitypub Networks

Reading Time: 4 min.
What are ActivityPub Networks? ActivityPub is a technical specification towards decentralized (more precisely, federated) social networking (termed the Fediverse) based upon the exchange of ActivityStreams messages that follow the Activity Vocabulary. The ActivityPub proposal has been standardized and published by the W3C and has motivated the design of several federated social networking systems. There are presently several concrete ActivityPub compliant implementations and the protocol sees meaningful adoption, primarily in the domain of federated social networks.
White Paper 14, Integrated Energy Accounting using Relational Databases

White Paper 14, Integrated Energy Accounting using Relational Databases

Reading Time: 3 min.
Open Risk White Paper 14: Integrated energy accounting using relational databases In this Open Risk White Paper we demonstrate a concrete implementation of an integrated energy accounting framework using relational database technologies. The framework enables accounting of non-financial disclosures (such as the physical and embodied energy footprints of economic transactions) while enforcing the familiar double-entry balance constraints used to produce conventional (monetary) accounts and financial statements. In addition, it allows enforcing constraints associated with the flow and transformations of energy that can happen inside the organizational perimeter.
White Paper 12, Deep-Linking Financial and Energy Accounting

White Paper 12, Deep-Linking Financial and Energy Accounting

Reading Time: 1 min.
Open Risk White Paper 12: Deep-Linking Financial and Energy Accounting We develop a conceptual framework for integrated accounting that produces (where possible) non-financial disclosures subject to the same double-entry balance constraints as those used to produce conventional financial statements and automatically ensures any additional conservation laws are satisfied. We identify the key ingredients required for such a rigorous integrated accounting framework, in terms of concepts, postulates and design choices. Our focus and concrete use case is built around energy accounting, keeping track on an entity’s detailed energy footprint (primary inputs, transformations and waste generation) as an extension of its standard financial accounting and reporting.
Connecting the Dots: Concentration, diversity, inequality and sparsity in economic networks

Connecting the Dots: Concentration, diversity, inequality and sparsity in economic networks

In this second Open Risk White Paper on "Connecting the Dots" we examine measures of concentration, diversity, inequality and sparsity in the context of economic systems represented as network (graph) structures.

Reading Time: 6 min.
Concentration, diversity, inequality and sparsity in the context of economic networks In this second Open Risk White Paper on Connecting the Dots we examine measures of concentration, diversity, inequality and sparsity in the context of economic systems represented as network (graph) structures. We adopt a stylized description of economies as property graphs and illustrate how relevant concepts can represent in this language. We explore in some detail data types representing economic network data and their statistical nature which is critical in their use in concentration analysis.
9 Ways Graphs Show Up in Data Science

9 Ways Graphs Show Up in Data Science

We explore a variety of distinct uses of graph structures in data science. We review various important graph types and sketch their linkages and relationships. The review provides an operational guide towards a better overall understanding of those powerful tools

Reading Time: 17 min.
Graphs seem to be everywhere in modern data science Graphs (and the related concept of Networks) have emerged from a relative mathematical and physics niche to an ubiquitous model for describing and interpreting various phenomena. While the scholarly account of how this came about would probably need a dedicated book, there is no doubt that one of the key factors that increased the visibility of the graph concept is the near universal adoption of digital social networks.
Towards the Semantic Description of Machine Learning Models

Towards the Semantic Description of Machine Learning Models

Reading Time: 7 min.
Semantic Web Technologies integrate naturally with the worlds of open data science and open source machine learning, empowering better control and management of the risks and opportunities that come with increased digitization and model use The ongoing and accelerating digitisation of many aspects of social and economic life means the proliferation of data driven/data intermediated decisions and the reliance on quantitative models of various sorts (going under various hashtags such as machine learning, artificial intelligence, data science etc.
An introduction to Semantic Python

An introduction to Semantic Python

A CrashCourse introduction to semantic data using Python covering a number of frameworks such as rdflib, owlready and pySHACL

Reading Time: 2 min.
Course Content This CrashCourse is an introduction to semantic data using Python. It covers the following topics: We learn to work with RDF graphs using rdflib We explore the owlready package and OWL ontologies We look into json-ld serialization of RDF/OWL data We try data validation using pySHACL We use throughout a realistic data set based on the Credit Ratings Ontology Who Is This Course For The course is useful to:
Non-Performing Loan Ontology

Non-Performing Loan Ontology

The NPL Ontology (NPLO) is a new ontology describing datasets of Non-Perfoming Loan Portfolios.

Reading Time: 4 min.
The Non-Perfoming Loan Ontology The Non-Performing Loan Ontology is a framework that aims to represent and categorize knowledge about non-performing loans using semantic web information technologies. Codenamed NPLO, it codifies the relationship between the various components of a Non-Performing Loan portfolio dataset.(NB: Non-performing loans are bank loans that are 90 days or more past their repayment date or that are unlikely to be repaid, for example if the borrower is facing financial difficulties).
Risk Function Ontology

Risk Function Ontology

The Risk Function Ontology (RFO) is a new ontology describing risk management roles (posts) and functions.

Reading Time: 3 min.
The Risk Function Ontology The Risk Function Ontology is a framework that aims to represent and categorize knowledge about risk management functions using semantic web information technologies. Codenamed RFO codifies the relationship between the various components of a risk management organization. Individuals, teams or even whole departments tasked with risk management exist in some shape or form in most organizations. The ontology allows the definition of risk management roles in more precise terms, which in turn can be used in a variety of contexts: towards better structured actual job descriptions, more accurate description of internal processes and easier inspection of alignement and consistency with risk taxonomies.
Risk Model Ontology

Risk Model Ontology

Reading Time: 2 min.
Semantic Web Technologies The Risk Model Ontology is a framework that aims to represent and categorize knowledge about risk models using semantic web information technologies. In principle any semantic technology can be the starting point for a risk model ontology. The Open Risk Manual adopts the W3C’s Web Ontology Language (OWL). OWL is a Semantic Web language designed to represent rich and complex knowledge about things, groups of things, and relations between things.
Version 0.2 of the Open Risk API incorporates the standardized EBA portfolio data templates

Version 0.2 of the Open Risk API incorporates the standardized EBA portfolio data templates

Reading Time: 2 min.
Extending the Open Risk API to include the EBA Portfolio Data Templates The Open Risk API provides a mechanism to integrate arbitrary collections of risk data and risk modelling resources in the context of assessing and managing financial risk. It is based on two key technologies of the modern Web, RESTful architectures and Semantic Data. OpenNPL, the credit portfolio management platfrom we launched recently fully integrates the latest versions of the Open Risk API.
From Big Data, to Linked Data and Linked Models

From Big Data, to Linked Data and Linked Models

Reading Time: 5 min.
From Big Data, to Linked Data and Linked Models The big data problem: As certainly as the sun will set today, the big data explosion will lead to a big clean-up mess How do we know? It is simply a case of history repeating. We only have to study the still smouldering last chapter of banking industry history. Currently banks are portrayed as something akin to the village idiot as far as technology adoption is concerned (and there is certainly a nugget of truth to this).