Authors:
Michael Schulze
1
;
2
and
Andreas Dengel
1
;
2
Affiliations:
1
Computer Science Department, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU), Germany
;
2
Smart Data & Knowledge Services Departm., Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Germany
Keyword(s):
Knowledge Graphs, RDF, OWL, Rule-Based Link Prediction, Case-Based Reasoning.
Abstract:
For the problem of credit account prediction on the basis of received invoices, this paper presents a pipeline consisting of 1) construction of an accounting knowledge graph, 2) enrichment algorithms, and 3), prediction of credit accounts with methods of a) rule-based link prediction, b) case-based reasoning, and c) a combination of both. Explainability and traceability have been key requirements. While preserving the order of invoices in cross-fold validation, key findings in our scenario are: 1) using all enrichments from the pipeline increases prediction performance up to 12.45 percent points, 2) single enrichments are useful on their own, 3) case-based reasoning benefits most from having enrichments available, and 4), the combination of link prediction and case-based reasoning yields best prediction results in our scenario. Paper page: https://git.opendfki.de/michael.schulze/account-prediction.