Rule-based Construction of Matching Processes

TL;DR

This paper introduces a self-configuring schema matching system that automatically adapts matching processes to specific mapping problems, reducing manual effort and improving mapping quality across various domains.

Contribution

The paper presents a novel self-configuring approach that analyzes schemas and intermediate results to automatically construct and adapt matching strategies for different mapping tasks.

Findings

System robustly produces high-quality mappings

Effective across schema, ontology, and model management domains

Reduces manual effort in constructing match strategies

Abstract

Mapping complex metadata structures is crucial in a number of domains such as data integration, ontology alignment or model management. To speed up that process automatic matching systems were developed to compute mapping suggestions that can be corrected by a user. However, constructing and tuning match strategies still requires a high manual effort by matching experts as well as correct mappings to evaluate generated mappings. We therefore propose a self-configuring schema matching system that is able to automatically adapt to the given mapping problem at hand. Our approach is based on analyzing the input schemas as well as intermediate matching results. A variety of matching rules use the analysis results to automatically construct and adapt an underlying matching process for a given match task. We comprehensively evaluate our approach on different mapping problems from the schema, ontology and model management domains. The evaluation shows that our system is able to robustly return good quality mappings across different mapping problems and domains.