We also study a strategy for receiving user feedback to assert some of the matchings generated and, relying on this feedback, improve the final result’s quality. In addition, we leverage constraints that arise in network scenarios to improve the quality of this data. To overcome the issue of requiring a large amount of training data, we also propose a bootstrapping procedure to generate training data automatically. We propose a family of methods for schema matching networks based on machine learning, which proved to be a competitive alternative for the traditional matching problem in several domains. The goal is to identify elements from several schemas that correspond to a single concept.
However, recently, there has been an increasing interest in matching several related schemas at once, a problem known as schema matching networks. Traditional instances of this problem involved a pair of schemas. This is a challenging problem since disparate elements in the schemas often represent the same concept. Schema matching is the problem of finding semantic correspondences between elements from different schemas. The experimental evaluation results that we have achieved using several benchmark datasets seem to show that our approach could be promising.
Moreover, our approach offers the possibility of being understood by a human operator and helping the processor to consume as little energy as possible. For this purpose, we rely on a symbolic regression model (implemented via genetic programming) that has been specifically trained to find the mathematical expression that can solve the ground truth provided by experts accurately.
Unlike the most recent developments based on deep learning, this study presents our research efforts on the development of novel methods for ontology matching that are accurate and interpretable at the same time. Ontology matching methods are of great importance today since they allow us to find the pivot points from which an automatic data integration process can be established. The problem of ontology matching consists of finding the semantic correspondences between two ontologies that, although belonging to the same domain, have been developed separately. The results show that eTuner produced tuned matching systems that achieve higher accuracy than using the systems with currently possible tuning methods. We employed eTuner to tune four recently developed matching systems on several real-world domains. While the tuning process is completely automatic, eTuner can also exploit user assistance (whenever available) to further improve the tuning quality. To increase the applicability of eTuner, we develop methods to tune a broad range of matching components. To efficiently search the huge space of tuning configurations, eTuner works sequentially, starting with tuning the lowest level components. Given a schema S, we match S against synthetic schemas, for which the ground truth mapping is known, and find a tuning that demonstrably improves the performance of matching S against real schemas. We describe eTuner, an approach to automatically tune schema matching systems. Tuning is skill and time intensive, but (as we show) without it the matching accuracy is significantly inferior. The domain user mustthen tune the system: select the right component to be executed and correctly adjust their numerous “knobs” (e.g., thresholds, formula coefficients). Most recent schema matching systems assemble multiple components, each employing a particular matching technique.