Erhard Rahm

A survey of approaches to automatic schema matching

Abstract. Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has significant limitations. On the other hand, previous research papers have proposed many techniques to achieve a partial automation of the match operation for specific application domains. We present a taxonomy that covers many of these existing approaches, and we describe the approaches in some detail. In particular, we distinguish between schema-level and instance-level, element-level and structure-level, and language-based and constraint-based matchers. Based on our classification we review some previous match implementations thereby indicating which part of the solution space they cover. We intend our taxonomy and review of past work to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component.

COMA: a system for flexible combination of schema matching approaches

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.

Schema and ontology matching with COMA

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.

Comparison of Schema Matching Evaluations

Recently, schema matching has found considerable interest in both research and practice. Determining matching components of database or XML schemas is needed in many applications, e.g. for E-business and data integration. Various schema matching systems have been developed to solve the problem semi-automatically. While there have been some evaluations, the overall effectiveness of currently available automatic schema matching systems is largely unclear. This is because the evaluations were conducted in diverse ways making it difficult to assess the effectiveness of each single system, let alone to compare their effectiveness. In this paper we survey recently published schema matching evaluations. For this purpose, we introduce the major criteria that influence the effectiveness of a schema matching approach and use these criteria to compare the various systems. Based on our observations, we discuss the requirements for future match implementations and evaluations.

Rondo: a programming platform for generic model management

Model management aims at reducing the amount of programming needed for the development of metadata-intensive applications. We present a first complete prototype of a generic model management system, in which high-level operators are used to manipulate models and mappings between models. We define the key conceptual structures: models, morphisms, and selectors, and describe their use and implementation. We specify the semantics of the known model-management operators applied to these structures, suggest new ones, and develop new algorithms for implementing the individual operators. We examine the solutions for two model-management tasks that involve manipulations of relational schemas, XML schemas, and SQL views.

AGENT WORK: a workflow system supporting rule-based workflow adaptation

Current workflow management systems still lack support for dynamic and automatic workflow adaptations. However, this functionality is a major requirement for next-generation workflow systems to provide sufficient flexibility to cope with unexpected failure events. We present the concepts and implementation of AGENTWORK, a workflow management system supporting automated workflow adaptations in a comprehensive way. A rule-based approach is followed to specify exceptions and necessary workflow adaptations. AGENTWORK uses temporal estimates to determine which remaining parts of running workflows are affected by an exception and is able to predictively perform suitable adaptations. This helps to ensure that necessary adaptations are performed in time with minimal user interaction which is especially valuable in complex applications such as for medical treatments.

Schema Matching and Mapping

Schema Matching and Mapping provides an overview of the ways in which the schema and ontology matching and mapping tools have addressed information systems requirements. Topics include effective methods for matching data, mapping transformation verification, mapping-driven schema evolution and merging.

Frameworks for entity matching: A comparison

Entity matching is a crucial and difficult task for data integration. Entity matching frameworks provide several methods and their combination to effectively solve different match tasks. In this paper, we comparatively analyze 11 proposed frameworks for entity matching. Our study considers both frameworks which do or do not utilize training data to semi-automatically find an entity matching strategy to solve a given match task. Moreover, we consider support for blocking and the combination of different match algorithms. We further study how the different frameworks have been evaluated. The study aims at exploring the current state of the art in research prototypes of entity matching frameworks and their evaluations. The proposed criteria should be helpful to identify promising framework approaches and enable categorizing and comparatively assessing additional entity matching frameworks and their evaluations.

Matching large schemas: Approaches and evaluation

Current schema matching approaches still have to improve for large and complex Schemas. The large search space increases the likelihood for false matches as well as execution times. Further difficulties for Schema matching are posed by the high expressive power and versatility of modern schema languages, in particular user-defined types and classes, component reuse capabilities, and support for distributed schemas and namespaces. To better assist the user in matching complex schemas, we have developed a new generic schema matching tool, COMA++, providing a library of individual matchers and a flexible infrastructure to combine the matchers and refine their results. Different match strategies can be applied including a new scalable approach to identify context-dependent correspondences between schemas with shared elements and a fragment-based match approach which decomposes a large match task into smaller tasks. We conducted a comprehensive evaluation of the match strategies using large e-Business standard schemas. Besides providing helpful insights for future match implementations, the evaluation demonstrated the practicability of our system for matching large schemas.

Evaluation of entity resolution approaches on real-world match problems

Despite the huge amount of recent research efforts on entity resolution (matching) there has not yet been a comparative evaluation on the relative effectiveness and efficiency of alternate approaches. We therefore present such an evaluation of existing implementations on challenging real-world match tasks. We consider approaches both with and without using machine learning to find suitable parameterization and combination of similarity functions. In addition to approaches from the research community we also consider a state-of-the-art commercial entity resolution implementation. Our results indicate significant quality and efficiency differences between different approaches. We also find that some challenging resolution tasks such as matching product entities from online shops are not sufficiently solved with conventional approaches based on the similarity of attribute values.