=Paper=
{{Paper
|id=Vol-3135/EcoFinKG_2022_short10
|storemode=property
|title=Improved Analysis of Survey Data using Knowledge Graphs
|pdfUrl=https://ceur-ws.org/Vol-3135/EcoFinKG_2022_short10.pdf
|volume=Vol-3135
|authors=Anna Gossen,Eike Nicklas
|dblpUrl=https://dblp.org/rec/conf/edbt/GossenN22
}}
==Improved Analysis of Survey Data using Knowledge Graphs==
https://ceur-ws.org/Vol-3135/EcoFinKG_2022_short10.pdf
Improved Analysis of Survey Data using Knowledge Graphs
Anna Gossen1,2 , Dr. Eike Nicklas1,2
1
Bank for International Settlements
2
HMS Analytical Software GmbH
Abstract
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.
Keywords
CEUR-WS, Knowledge Graphs, Ontologies
Knowledge Graphs are widely used in financial sector different 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 ver-
needs in the growing markets environment. By introduc- sions, provenance and data access information. The on-
ing semantically meaningful meta data and integrating tology reuses W3C standards and open linked vocabular-
instance data with the contextual structure, knowledge ies (SKOS1 , PROV-O2 , DCAT3 ), which makes it easy to
graphs offer a smart and efficient 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 efficient
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 offers:
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, effort, 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 heteroge-
taking 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
files. 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 analy-
agement and analysis of data on various topics and with sis work in the context of central banking organization
and governance.
Published in the Workshop Proceedings of the EDBT/ICDT 2022 Joint
Conference (March 29-April 1, 2022), Edinburgh, UK
" anna.gossen@bis.org (A. Gossen); eike.nicklas@bis.org
(Dr. E. Nicklas) 1
https://www.w3.org/TR/2009/REC-skos-reference-20090818/
© 2022 Copyright for this paper by its authors. Use permitted under Creative 2
Commons License Attribution 4.0 International (CC BY 4.0). https://www.w3.org/TR/prov-o/
CEUR Workshop Proceedings (CEUR-WS.org) 3
https://www.w3.org/TR/vocab-dcat-2/
CEUR
http://ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Figure 1: Schematic overview of the main concepts in the dataset ontology.