Abstract. Most of the schema matching tools are assembled from multiple match algorithms, each employing a particular technique to improve matching accuracy and making matching systems extensible and customizable to a particular domain. Recently, it has been pointed out that the main issue is how to select the most suitable match algorithms to execute for a given domain and how to adjust the multiple knobs (e.g. threshold, performance, quality, etc.). The solutions provided by current schema matching tools consist in aggregating the results obtained by several match algorithms to improve the quality of the discovered matches. However, aggregation entails several drawbacks. In this article, we present a novel method for combining schema matching algorithms. The matching engine makes use of a decision tree to combine the most appropriate match algorithms. As a first consequence of using the decision tree, the performance of the system is improved since the complexity is bounded by the height of the decision tree. Thus, only a subset of these match algorithms is used during the matching process. The second advantage is the improvement of the quality of matches. Indeed, for a given domain, only the most suitable match algorithms are used. The experiments show the effectiveness of our approach w.r.t. other matching tools.
Abstract. Constraint programming is rapidly becoming the technology of choice for modeling and solving complex combinatorial problems. However, users of constraint programming technology need significant expertise in order to model their problem appropriately. The lack of availability of such expertise can be a significant bottleneck to the broader uptake of constraint technology in the real world. In this paper we are concerned with automating the formulation of constraint satisfaction problems from examples of solutions and non-solutions. We combine techniques from the fields of machine learning and constraint programming. In particular we present a portfolio of approaches to exploiting the semantics of the constraints that we acquire to improve the efficiency of the acquisition process. We demonstrate how inference and search can be used to extract useful information that would otherwise be hidden in the set of examples from which we learn the target constraint satisfaction problem. We demonstrate the utility of the approaches on a case-study domain.
This paper presents a technique for learning parameterized implied constraints. They can be added to a model to improve the solving process. Experiments on implied Gcc constraints show the interest of our approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.