SR-Match: A Novel Schema Matcher Based on Semantic Relationship
05 Aug 2011-pp 93-102
TL;DR: A new and efficient Semantic-Relationship schema matching (SR-Match) approach which considers the semantic relationships as one of the parameters for matching and, if both semantics and relationships are taken into account, the degree of accuracy in matching results is improved.
Abstract: In data integration, schema matching plays an important role Present schema matching tools combine various match algorithms, each employing a particular technique to improve matching accuracy However there is still no fully automatic tool is available and also there is lack of accuracy As a step in this direction, we have proposed a new and efficient Semantic-Relationship schema matching (SR-Match) approach which considers the semantic relationships as one of the parameters for matching Here in SR-Match, the initial mappings performed by the basic schema mapping techniques, acts as input to the relationship matcher Relationship matcher compares the remaining unmapped elements based on their semantic relationship with their parents It is observed that, if both semantics and relationships are taken into account, the degree of accuracy in matching results is improved
References
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26 Feb 2002
TL;DR: This paper presents a matching algorithm based on a fixpoint computation that is usable across different scenarios and conducts a user study, in which the accuracy metric was used to estimate the labor savings that the users could obtain by utilizing the algorithm to obtain an 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.
1,613 citations
Proceedings Article•
11 Sep 2001TL;DR: This paper proposes a new algorithm, Cupid, that discovers mappings between schema elements based on their names, data types, constraints, and schema structure, using a broader set of techniques than past approaches.
Abstract: Schema matching is a critical step in many applications, such as XML message mapping, data warehouse loading, and schema integration. In this paper, we investigate algorithms for generic schema matching, outside of any particular data model or application. We first present a taxonomy for past solutions, showing that a rich range of techniques is available. We then propose a new algorithm, Cupid, that discovers mappings between schema elements based on their names, data types, constraints, and schema structure, using a broader set of techniques than past approaches. Some of our innovations are the integrated use of linguistic and structural matching, context-dependent matching of shared types, and a bias toward leaf structure where much of the schema content resides. After describing our algorithm, we present experimental results that compare Cupid to two other schema matching systems.
1,533 citations
TL;DR: This paper presents a new classification of schema-based matching techniques that builds on the top of state of the art in both schema and ontology matching and distinguishes between approximate and exact techniques at schema-level; and syntactic, semantic, and external techniques at element- and structure-level.
Abstract: Schema and ontology matching is a critical problem in many application domains, such as semantic web, schema/ontology integration, data warehouses, e-commerce, etc. Many different matching solutions have been proposed so far. In this paper we present a new classification of schema-based matching techniques that builds on the top of state of the art in both schema and ontology matching. Some innovations are in introducing new criteria which are based on (i) general properties of matching techniques, (ii) interpretation of input information, and (iii) the kind of input information. In particular, we distinguish between approximate and exact techniques at schema-level; and syntactic, semantic, and external techniques at element- and structure-level. Based on the classification proposed we overview some of the recent schema/ontology matching systems pointing which part of the solution space they cover. The proposed classification provides a common conceptual basis, and, hence, can be used for comparing different existing schema/ontology matching techniques and systems as well as for designing new ones, taking advantages of state of the art solutions.
1,285 citations
20 Aug 2002
TL;DR: This work develops the COMA schema matching system as a platform to combine multiple matchers in a flexible way and uses COMA as a framework to comprehensively evaluate the effectiveness of different matchers and their combinations for real-world schemas.
Abstract: Schema matching is the task of finding semantic correspondences between elements of two schemas. It is needed in many database applications, such as integration of web data sources, data warehouse loading and XML message mapping. To reduce the amount of user effort as much as possible, automatic approaches combining several match techniques are required. While such match approaches have found considerable interest recently, the problem of how to best combine different match algorithms still requires further work. We have thus developed the COMA schema matching system as a platform to combine multiple matchers in a flexible way. We provide a large spectrum of individual matchers, in particular a novel approach aiming at reusing results from previous match operations, and several mechanisms to combine the results of matcher executions. We use COMA as a framework to comprehensively evaluate the effectiveness of different matchers and their combinations for real-world schemas. The results obtained so far show the superiority of combined match approaches and indicate the high value of reuse-oriented strategies.
1,199 citations
14 Jun 2005
TL;DR: Different match strategies can be applied including various forms of reusing previously determined match results and a so-called fragment-based match approach which decomposes a large match problem into smaller problems.
Abstract: We demonstrate the schema and ontology matching tool COMA++. It extends our previous prototype COMA utilizing a composite approach to combine different match algorithms [3]. COMA++ implements significant improvements and offers a comprehensive infrastructure to solve large real-world match problems. It comes with a graphical interface enabling a variety of user interactions. Using a generic data representation, COMA++ uniformly supports schemas and ontologies, e.g. the powerful standard languages W3C XML Schema and OWL. COMA++ includes new approaches for ontology matching, in particular the utilization of shared taxonomies. Furthermore, different match strategies can be applied including various forms of reusing previously determined match results and a so-called fragment-based match approach which decomposes a large match problem into smaller problems. Finally, COMA++ cannot only be used to solve match problems but also to comparatively evaluate the effectiveness of different match algorithms and strategies.
683 citations