Order Matters: Matching Multiple Knowledge Graphs

TL;DR

This paper investigates methods to efficiently match multiple knowledge graphs by reducing multi-source matching to linear binary matching tasks, emphasizing the importance of matching order and strategy for near-optimal results.

Contribution

It introduces approaches to transform multi-knowledge graph matching into linear binary matchings, highlighting the impact of order and strategy on performance.

Findings

Matching order significantly affects results

Linear strategies can achieve near-optimal matching

Multi-source strategies reduce computational effort

Abstract

Knowledge graphs (KGs) provide information in machine interpretable form. In cases where multiple KGs are used in the same system, that information needs to be integrated. This is usually done by automated matching systems. Most of those systems consider only 1:1 (binary) matching tasks. Thus, matching a larger number of knowledge graphs with such systems would lead to quadratic efforts. In this paper, we empirically analyze different approaches to reduce the task of multi-source matching to a linear number of executions of binary matching systems. We show that the matching order of KGs and the multi-source strategy actually matter and that near-optimal results can be achieved with linear efforts.