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.
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 (See also live version and white paper (OpenRiskWP04_061415)
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).