Order Matters: Matching Multiple Knowledge Graphs
This work addresses the integration challenge for systems using multiple knowledge graphs, offering a practical solution to reduce computational costs, though it is incremental as it builds on existing binary matching methods.
The paper tackles the problem of efficiently matching multiple knowledge graphs by reducing the quadratic complexity of binary matching systems to linear efforts, showing that near-optimal results can be achieved with careful matching order and strategy.
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.