In this work, we present an internal knowledge-graph-based application for the storage and analysis of data regarding central banking practice on governance, management and organizational matters. We discuss a custom ontology as well as the high level application architecture and implementation challenges we experienced. Knowledge Graphs are widely used in financial sector diferent data structures, some of which is collected via for various purposes, including fraud detection, banking surveys. Its core is a custom Dataset ontology, which oversight or customer profiling. Traditional database provides generic data structures to store information technologies often do not solve expanding analytical about given entities including revisions, temporal verneeds in the growing markets environment. By introduc- sions, provenance and data access information. The oning semantically meaningful meta data and integrating tology reuses W3C standards and open linked vocabularinstance data with the contextual structure, knowledge ies (SKOS1, PROV-O2, DCAT3), which makes it easy to graphs ofer a smart and eficient way to create, store, understand and to apply. Figure 1 provides a high level query, analyze data and convert it into direct value. overview. In the context of central banking, the analysis of gov- Extensions to this ontology allow for the storage of ernance and organizational structures and processes in- additional metadata. For example, referenced entities and volves various challenges due to heterogeneous, but concepts can be structured in taxonomies for eficient strongly inter-connected organizational structures and data selection and aggregation, supported by standard often qualitative or textual data. In addition, hierarchical ontologies such as the ORG ontology. In addition, related metadata is commonly required for grouping or aggrega- data can be grouped in datasets, e.g. to trace data that tion of analytical results. was collected in the same survey. An exemplary use case demonstrating these challenges Supported by the use knowledge graphs technologies, is the following: the application ofers: The data analysts need to find central banks that have a supervisory board, where the governor is not chair, and a • Generic, ontology-driven data analysis dedicated Monetary Policy Committee, where the governor • Advanced, inference-based full text search and is chair, and at least one member contextual filtering The task will require a lot of time, efort, resources, and • Data provenance tracking table join operations when using a standard relational • Time-based data analysis database. This exercise gets even more complicated when • Seamless datasets integration from heterogetaking temporal aspects into account. In addition, the neous sources data required for this analysis is often not available in • Data quality validation one database, but distributed across multiple systems or ifles. In this presentation, we describe the generic dataset In this work, we present an internal knowledge-graph- ontology and a domain-specific extension, as well as the based application for the storage and analysis of data high-level application architecture. In addition, we will regarding central banking practice on governance, man- share experiences regarding implementation challenges agement and organizational matters. It supports the man- and discuss how the application supports the data analyagement and analysis of data on various topics and with sis work in the context of central banking organization and governance.
Figure 1: Schematic overview of the main concepts in the dataset ontology.