International Monetary Fund

The IMF works to achieve sustainable growth for its approximately 200 member countries. It carries out missions and loans funds to execute projects for the member countries. The countries’ financial situation are measured and tracked using a wide variety of economic indicators.

The challenge: Information is stored in a wide variety of vocabulary managers, applications and databases. This makes it difficult to quickly get answers to questions required to carry out day to day work. For example,

Who is likely to be an expert on customs?
Find documents associated with countries similar to Afghanistan?
What types of missions for what countries are addressing climate change?

In each case, getting the answer requires retrieving and processing information from multiple sources. To represent the information required to answer the questions, we built an ontology that covers the core business of the IMF.  The main things are:

  • Organizations and People
  • Geographic regions, Countries and Country Groups
  • Missions that produce Documents for Countries
  • Documents about Topics authored by Persons
  • Economic Indicators & Measurements

We created a knowledge graph composed of the ontology and RDF triples data that was created by converting taxonomies and datasets from a variety of data sources. We wrote SPARQL queries that traverse the knowledge graph to answer the questions of interest.  This is to be the basis for an internal knowledge portal for integrating structured data (such as GDP per country) with unstructured data (such as country-specific reports on commodity prices).