Similarity flooding: a versatile graph matching algorithm and its application to schema matching

TLDR

Matching elements of two data schemas or data instances is crucial for data warehousing, e‑business, and biochemical applications. The paper presents a matching algorithm based on a fixpoint computation that is usable across different scenarios. The algorithm takes two graphs as input, computes a mapping via fixpoint iteration, selects a subset with filters, and its accuracy is assessed by counting required human adjustments, while it is also deployed as a high‑level operator in an information‑model testbed. A user study showed that the accuracy metric could estimate labor savings users would gain by using the algorithm for initial matching.

Abstract

Matching elements of two data schemas or two data instances plays a key role in data warehousing, e-business, or even biochemical applications. In this paper we present a matching algorithm based on a fixpoint computation that is usable across different scenarios. The algorithm takes two graphs (schemas, catalogs, or other data structures) as input, and produces as output a mapping between corresponding nodes of the graphs. Depending on the matching goal, a subset of the mapping is chosen using filters. After our algorithm runs, we expect a human to check and if necessary adjust the results. As a matter of fact, we evaluate the 'accuracy' of the algorithm by counting the number of needed adjustments. We conducted a user study, in which our accuracy metric was used to estimate the labor savings that the users could obtain by utilizing our algorithm to obtain an initial matching. Finally, we illustrate how our matching algorithm is deployed as one of several high-level operators in an implemented testbed for managing information models and mappings.