SUBMASSIVE: Resolving Subclass Cycles in Very Large Knowledge Graphs

TL;DR

This paper introduces SUBMASSIVE, an automated reasoning approach using MAXSAT solvers to detect and resolve cyclic subclass relations in large knowledge graphs, improving data consistency.

Contribution

It presents a scalable, automated method for fixing cyclic subclass relations in large knowledge graphs, addressing errors in class hierarchies.

Findings

The approach effectively detects and resolves subclass cycles.

Trade-offs exist between the number of triples removed and algorithm efficiency.

Tested on the LOD-a-lot dataset, showing practical applicability.

Abstract

Large knowledge graphs capture information of a large number of entities and their relations. Among the many relations they capture, class subsumption assertions are usually present and expressed using the \texttt{rdfs:subClassOf} construct. From our examination, publicly available knowledge graphs contain many potentially erroneous cyclic subclass relations, a problem that can be exacerbated when different knowledge graphs are integrated as Linked Open Data. In this paper, we present an automatic approach for resolving such cycles at scale using automated reasoning by encoding the problem of cycle-resolving to a MAXSAT solver. The approach is tested on the LOD-a-lot dataset, and compared against a semi-automatic version of our algorithm. We show how the number of removed triples is a trade-off against the efficiency of the algorithm.