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.

Semantic integration has been a long-standing challenge for the database community. It has received steady attention over the past two decades, and has now become a prominent area of database research. In this article, we first review database applications that require semantic integration, and discuss the difficulties underlying the integration process. We then describe recent progress and identify open research issues. We will focus in particular on schema matching, a topic that has received much attention in the database community, but will also discuss data matching (e.g., tuple deduplication), and open issues beyond the match discovery context (e.g., reasoning with matches, match verification and repair, and reconciling inconsistent data values). For previous surveys of database research on semantic integration, see (Rahm & Bernstein 2001;

Most recent schema matching systems assemble multiple components, each employing a particular matching technique. The domain user must then tune the system: select the right component to be executed and correctly adjust their numerous "knobs" (e.g., thresholds, formula coefficients). Tuning is skill- and time-intensive, but (as we show) without it the matching accuracy is significantly inferior. We describe

This dissertation studies representation matching: the problem of creating semantic mappings between two data representations. Examples of data representations are relational schemas, ontologies, and XML DTDs. Examples of semantic mappings include "element location of one representation maps to element address of the other", "contact-phone maps to agent-phone", and "listed-price maps to price * (1 + tax-rate)"...

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

Abstract. Finding mappings between compatible ontologies is an important but difficult open problem. Instance-based methods for solving this problem have the advantage of focusing on the most active parts of the ontologies and reflect concept semantics as they are actually being used. However such methods have not at present been widely investigated in ontology mapping, compared to linguistic and structural techniques. Furthermore, previous instance-based mapping techniques were only applicable to cases where a substantial set of instances was available that was doubly annotated with both vocabularies. In this paper we approach the mapping problem as a classification problem based on the similarity between instances of concepts. This has the advantage that no doubly annotated instances are required, so that the method can be applied to any two corpora annotated with their own vocabularies. We evaluate the resulting classifiers on two real-world use cases, one with homogeneous and one with heterogeneous instances. The results illustrate the efficiency and generality of this method. 1

This evaluation of the state-of-the-art schema matching approaches is based on a comprehensive testing of modern systems on various real-world schemas.

Pervasive business requires information brokers that support customer/supplier enterprise system interactions in sensible ways. We present a summary of our model for pervasive business: a virtual database used to aggregate information from parts of multiple enterprise systems. Aggregated data is typically managed by a brokering enterprise in a high performance main-memory database. Replicated information from multiple customer and supplier enterprise systems is managed by the broker. This data is acquired and updated seamlessly by the broker's Enterprise Systems Logic^TM, using a variety of technologies to acquire the data, translate it into a canonical form and apply updates to the originating system when appropriate. We present the motivation for this novel pervasive business architecture, a review of related systems and technologies and our work to date on this project.

Abstract. This paper summarizes the results of PRIOR system, which is an ontology mapping system based on Profile pRopagation and InfOrmation Retrieval techniques, for OAEI 2006 campaign. The PRIOR system exploits both linguistic and structural information to map small ontologies, and integrates Indri search engine to process large ontologies. The preliminary results of the experiments for four tasks (i.e. benchmark, web directories, anatomy and food) are presented. A discussion of the results and future work are given at the end. 1 Presentation of the system 1.1 State, purpose, general statement The World Wide Web (WWW) makes a large number of digital resources publicly accessible. However, finding relevant information, i.e. searching for digital resources from various sources and manually organizing them for relevance, becomes more and more intractable. Semantic interoperability research is aimed at enabling different information systems to communicate information consistently with the intended

Abstract. One significant part of today’s Web is Web databases, which can dynamically provide information in response to user queries. To help users submit queries to and collect query results from different Web databases, the query interface matching problem needs to be addressed. To solve this problem, we propose a new complex schema matching approach, Holistic Schema Matching (HSM). By examining the query interfaces of real Web databases, we observe that attribute matchings can be discovered from attribute-occurrence patterns. For example, First Name often appears together with Last Name while it is rarely co-present with Author in the Books domain. Thus, we design a count-based greedy algorithm to identify which attributes are more likely to be matched in the query interfaces. In particular, HSM can identify both simple matching and complex matching, where the former refers to 1:1 matching between attributes and the latter refers to 1:n or m:n matching between attributes. Our experiments show that HSM can discover both simple and complex matchings accurately and efficiently on real data sets. 1