SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks

Li, Wen-Syan; Clifton, Chris

doi:10.1016/s0169-023x(99)00044-0

Cited by 268 publications

(180 citation statements)

References 35 publications

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“…Two recent works, however, share with Autoplex the general goal of accelerating the mapping process. In [15] a neural network-based method is described, that classifies attributes of data sources. This important integration step is also part of the Autoplex discovery process.…”

Section: Introductionmentioning

confidence: 99%

Autoplex: Automated Discovery of Content for Virtual Databases

Berlin

Motro

2001

Cooperative Information Systems

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Abstract. Most virtual database systems are suitable for environments in which the set of member information sources is small and stable. Consequently, present virtual database systems do not scale up very well. The main reason is the complexity and cost of incorporating new information sources into the virtual database. In this paper we describe a system, called Autoplex, which uses machine learning techniques for automating the discovery of new content for virtual database systems. Autoplex assumes that several information sources have already been incorporated ("mapped") into the virtual database system by human experts (as done in standard virtual database systems). Autoplex learns the features of these examples. It then applies this knowledge to new candidate sources, trying to infer views that "resemble" the examples. In this paper we report initial results from the Autoplex project.

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Section: Introductionmentioning

confidence: 99%

Autoplex: Automated Discovery of Content for Virtual Databases

Berlin

Motro

2001

Cooperative Information Systems

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“…Manually constructing the mappings is extremely labor intensive and error prone. Hence, numerous works have leveraged a broad variety of techniques, including mining ones, to automatically create semantic mappings (e.g., [76,86,75,67,15,74,83,77,84,62,5,6,79,35], see also [87,4] for surveys).…”

Section: Querying With Information Processing Systemsmentioning

confidence: 99%

Mining for Information Discovery on the Web: Overview and Illustrative Research

Yu¹,

Doan²

2004

Intelligent Technologies for Information Analysis

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Summary. The Web has become a fertile ground for numerous research activities in mining. In this chapter we discuss research on finding targeted information on the Web. First, we briefly survey the research area. We focus in particular on two key issues: (a) mining to impose structures over Web data, for example by building taxonomies and portals, to aid in Web navigation, and (b) mining to build information processing systems, such as search engines, question answering systems, and data integration ones. Next, we describe two recent Web mining projects that illustrate the use of mining techniques to address the above two key issues. We conclude by briefly discussing novel research opportunities in the area of mining for information discovery on the Web.

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“…Schema matching has been considered as a separate research problem and its related challenges have been addressed by a number of research and development projects, including Aumueller et al [5], Do and Rahm [18], Giunchiglia et al [27], Li and Clifton [39], Li et al [40], Madhavan et al [41], Melnik et al [44], Miller et al [47], Wang et al [64]. In addition to these projects, there are several other efforts that consider some specific features of schema matching.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated schema integration with SASMINT

Unal

2009

Knowl Inf Syst

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The emergence of increasing number of collaborating organizations has made clear the need for supporting interoperability infrastructures, enabling sharing and exchange of data among organizations. Schema matching and schema integration are the crucial components of the interoperability infrastructures, and their semi-automation to interrelate or integrate heterogeneous and autonomous databases in collaborative networks is desired. The Semi-Automatic Schema Matching and INTegration (SASMINT) System introduced in this paper identifies and resolves several important syntactic, semantic, and structural conflicts among schemas of relational databases to find their likely matches automatically. Furthermore, after getting the user validation on the matched results, it proposes an integrated schema. SASMINT uses a combination of a variety of metrics and algorithms from the Natural Language Processing and Graph Theory domains for its schema matching. For the schema integration, it utilizes a number of derivation rules defined in the scope of the research work explained in this paper. Furthermore, a derivation language called SASMINT Derivation Markup Language (SDML) is defined for capturing and formulating both the results of matching and the integration that can be further used, for example for federated query processing from independent databases. In summary, the paper focuses on addressing: (1) conflicts among schemas that make automatic schema matching and integration difficult, (2) the main components of the SASMINT approach and system, (3) in-depth exploration of SDML, (4) heuristic rules designed and implemented as part of the schema integration component of the SASMINT system, and (5) experimental evaluation of SASMINT.

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SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks

Cited by 268 publications

References 35 publications

Autoplex: Automated Discovery of Content for Virtual Databases

Autoplex: Automated Discovery of Content for Virtual Databases

Mining for Information Discovery on the Web: Overview and Illustrative Research

Semi-automated schema integration with SASMINT

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